Publikationen von Prof. Dr. Dr. h.c. Peter Maaß

Monografien (4)

1. H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.).
   German Success Stories in Industrial Mathematics .
   Mathematics in Industrie, 167 Seiten, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7

2. R. Dahlhaus, J. Kurths, P. Maaß, J. Timmer (Hrsg.).
   Mathematical Methods in Time Series Analysis and Digital Image Processing.
   Understanding Complex Systems, 294 Seiten, Springer Verlag, 2008.
3. A. K. Louis, P. Maaß, A. Rieder.
   Wavelets. Theorie und Anwendungen.
   Studienbücher Mathematik, 330 Seiten, Teubner Verlag, 1998.
4. A. K. Louis, P. Maaß, A. Rieder.
   Wavelets: Theory and Applications.
   Pure and Applied Mathematics, 342 Seiten, WILEY-VCH, 1997.

Buchkapitel (32)

1. P. Maaß, L. Hauberg-Lotte, T. Boskamp.
   MALDI Imaging: Exploring the Molecular Landscape.
   German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 97-103, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7_17

2. P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
   Mathematische Architekturen für Neuronale Netze.
   Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.

   DOI: 10.1007/s00591-022-00325-y

3. J. Behrmann, M. Schmidt, J. Wildner, P. Maaß, S. Schmale.
   Purity Assessment of Pellets Using Deep Learning.
   German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 29-34, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7_6

4. P. Maaß.
   Deep learning for trivial inverse problems.
   Compressed Sensing and its Applications, H. Boche, R. Calderbank, G. Caire, G. Kutyniok, R. Mathar, P. Petersen (Hrsg.), Applied and Numerical Harmonic Analysis, S. 195-209, Birkhäuser, 2019.

   DOI: 10.1007/978-3-319-73074-5_6

5. O. Riemer, P. Maaß, F. E. Elsner-Dörge, P. Gralla, J. Vehmeyer, M. Willert, A. Meier, I. Zahn.
   Predictive compensation measures for the prevention of shape deviations of mircomilled dental products.
   Cold Micro Metal Forming, Springer Verlag, 2018.
6. B. Denkena, P. Maaß, A. Schmidt, D. Niederwestberg, J. Vehmeyer, C. Niebuhr, P. Gralla.
   Thermomechanical Deformation of Complex Workpieces in Milling and Drilling Processes.
   Thermal Effects in Complex Machining Processes - Final Report of the DFG Priority Program 1480, D. Biermann, F. Hollmann (Hrsg.), LNPE, S. 219-250, Springer Verlag, 2017.
7. R. Ressel, P. Dülk, S. Dahlke, K. Kazimierski, P. Maaß.
   Regularity of the Parameter-to-State Map of a Parabolic Partial Differential Equation.
   Extraction of Quantifiable Information from Complex Systems, S. Dahlke, W. Dahmen, M. Griebel, W. Hackbusch, K. Ritter, R. Schneider, C. Schwab, H. Yserentant (Hrsg.), Lecture Notes in Computational Science and Engineering, S. 53-67, Springer Verlag, 2014.

   DOI: 10.1007/978-3-319-08159-5_3

8. M. Andres, H. Blum, C. Brandt, C. Carstensen, P. Maaß, J. Niebsch, A. Rademacher, R. Ramlau, A. Schröder, E. Stephan, S. Wiedemann.
   Adaptive finite Elements and Mathematical Optimization Methods.
   Process Machine Interactions, B. Denkena, F. Hollmann (Hrsg.), Lecture Notes in Production Engineering, S. 53-77, Springer Verlag, 2013.

   DOI: 10.1007/978-3-642-32448-2

9. I. Piotrowska-Kurczewski, C. Brandt, P. Maaß, O. Riemer.
   Inverse Modelling.
   Micro Metal Forming, F. Vollertsen (Hrsg.), ISBN: 978-3-642-30915-1, S. 267-288, Springer Verlag, 2013.
10. C. Brandt, A. Krause, J. Niebsch, J. Vehmeyer, E. Brinksmeier, P. Maaß, R. Ramlau.
    Surface Generation Process with Consideration of the Balancing State in Diamond Machining.
    Process Machine Interactions, B. Denkena, F. Hollmann (Hrsg.), Lecture Notes in Production Engineering, S. 329-360, Springer Verlag, 2013.

    DOI: 10.1007/978-3-642-32448-2

11. G. J. Bauer, D. Lorenz, P. Maaß, H. Preckel, D. Trede.
    Compounds, Drugs and Mathematical Image Processing.
    Production Factor Mathematics, M. Grötschel, K. Lucas, V. Mehrmann (Hrsg.), S. 379-392, Springer Verlag, 2010.

    DOI: 10.1007/978-3-642-11248-5_20

12. P. Maaß, O. Riemer.
    Flexible Herstellung von tribologisch optimierten Mikroumformwerkzeugen.
    4. Kolloquium Mikroproduktion, F. Vollertsen (Hrsg.), S. 281-286, BIAS Verlag, 2009.
13. S. Dahlke, P. Maaß, G. Teschke, K. Koch, D. Lorenz, S. Müller, S. Schiffler, A. Stämpfli, H. Thiele, M. Werner.
    Multiscale Approximation.
    Mathematical Methods in Time Series Analysis and Digital Image Processing, R. Dahlhaus, J. Kurths, P. Maaß, J. Timmer (Hrsg.), S. 75-109, Springer Verlag, 2008.
14. G. J. Bauer, D. Lorenz, P. Maaß, H. Preckel, D. Trede.
    Wirkstoffe, Medikamente und mathematische Bildverarbeitung.
    Produktionsfaktor Mathematik - wie Mathematik Technik und Wirtschaft bewegt, M. Grötschel, K. Lucas, V. Mehrmann (Hrsg.), acatech DISKUTIERT, S. 461-477, acatech - Deutsche Akademie der Technikwissenschaften, 2008.

    DOI: 10.1007/978-3-540-89435-3_20

15. M. Kulesh, B. Berkels, K. Bredies, C. S. Garbe, J. F. Acker, M. S. Diallo, M. Droske, M. Holschneider, J. Hron, C. Kondermann, P. Maaß, N. Olischläger, H. Peitgen, T. Preusser, M. Rumpf, F. Scherbaum, S. Turek.
    Inverse Problems and Parameter Identification in Image Processing.
    Mathematical Methods in Signal Processing and Digital Image Analysis, R. Dahlhaus, J. Kurths, P. Maaß, J. Timmer (Hrsg.), S. 111-151, Springer Verlag, 2007.

    DOI: 10.1007/978-3-540-75632-3

16. K. Bredies, D. Lorenz, P. Maaß, G. Teschke.
    A partial differential equation for continuous non-linear shrinkage filtering and its application for analyzing MMG data.
    Wavelet Applications in Industrial Processing, F. Truchetet (Hrsg.), 5266, S. 84-93, SPIE, 2004.

    DOI: 10.1117/12.515945

17. S. Dahlke, P. Maaß.
    An outline of adaptive wavelet galerkin methods for Tikhonov regularization of inverse parabolic problems.
    Recent Development in Theories and Numerics, Y. Hon, M. Yamamoto, J. Cheng, J. Lee (Hrsg.), S. pp. 56-66, World Scientific, 2003.

    DOI: 10.1142/9789812704924_0006

18. P. Maaß, C. Boeckmann, A. Mekler.
    Improvement of environment observing remote sensing devices by regularization techniques.
    Mathematics - key technology for the futore, W. Jäger, H. Krebs (Hrsg.), S. 162-172, Springer Verlag, 2003.
19. V. Dicken, I. Menz, J. Niebsch, P. Maaß, R. Ramlau.
    Inverse Unwuchtidentifikation an Flugtriebwerken mit Quetschöldämpfern.
    Schwingungen in rotierenden Machinen VI, Sirm 2003, A. Irretier, R. Nordmann, H. Springer (Hrsg.), S. 123-137, Vieweg Verlag, 2003.
20. P. Maaß, M. Ende, D. Kayser, W. Osten, G. Teschke.
    Continuous wavelet methods in signal processing.
    Fringe 2001: 4th International Workshop on Automatic Processing of Fringe Patterns, W. Osten, W. Jüptner (Hrsg.), S. 142-153, Elsevier, 2001.
21. T. Köhler, P. Maaß, M. Seebass, P. Wust.
    Efficient methods for Hyperthermia treatment planning.
    Surveys on Solution Methods for Inverse Problems, D. Colton, H. W. Engl, A. K. Louis, J. R. Laughlin, W. Rundell (Hrsg.), S. 155-168, Springer Verlag, 2000.
22. P. Maaß.
    Wavelet Methods and Applications in Physics.
    Advanced Mathematical Methods in Metrology IV, P. Ciarlini, M. G. Cox, F. Pavese, D. Richter (Hrsg.), World Scientific, 1998.
23. P. Maaß, T. Boskamp, V. Dicken, R. Bischoff, H. Peters, H. Stark.
    Bilddatenkompression mit Wavelet-Methoden.
    Mathematik: Schlüsseltechnologie für die Zukunft, Verbundprojekte zwischen Universität und Industrie, F. Hoffmann, W. Jäger, B. Lohmann, H. Schunck (Hrsg.), S. 385-394, Springer Verlag, 1997.
24. P. Maaß.
    Wavelet Methods in Signal Processing.
    Advanced Mathimatical Tools in Metrology III, P. Ciarlini, M. G. Cox, F. Pavese, D. Richter (Hrsg.), S. 91-104, World Scientific, 1997.
25. P. Maaß, A. Rieder.
    Wavelet-accelerated Tikholov-regularisation with applications.
    Inverse Peoblems in Medical Imaging and Nondestructive Testing, H. W. Engl, A. K. Louis, W. Rundell (Hrsg.), S. 134-159, Springer Verlag, 1997.
26. M. Ende, A. K. Louis, P. Maaß, G. Mayer-Kress.
    EEG signal analysis by continuous wavelet transform techniques.
    Nonlinear Analysis of Physiological Data, H. Kantz, G. Mayer-Kress, J. Kurths (Hrsg.), S. 213-219, Springer Verlag, 1996.
27. P. Maaß.
    Wavelet-projection methods for inverse problems.
    Beiträge zur Angewandten Analysis und Numerik, E. Schock (Hrsg.), S. 231-224, Shaker Verlag, 1994.
28. H. Hammer, P. Maaß, A. Rieder, J. Meyer.
    Wavelet Analysis of Auscultatory Blood Pressure Signals.
    Abstracts of the second European conference on Engineering and Medicine, S. 322-323, Elsevier, 1993.
29. P. Maaß.
    Wideband approximation and Wavelet Transform.
    Radar and Sonar, Part II, F. A. Grünbaum, M. Bernfeld, R. E. Blahut (Hrsg.), S. 83-88, Springer Verlag, 1992.
30. P. Maaß.
    Singular value decompositions for Radon transforms.
    Mathematical Aspects of Computerized Tomography, G. T. Herman, A. K. Louis, F. Natterer (Hrsg.), Lecture Notes in Mathematics, S. 6-14, Springer Verlag, 1991.

    DOI: 10.1007/BFb0084504

31. P. Maaß.
    A generalized radon transform in wideband radar.
    Integral Geometry and Tomography, E. Grinberg, E. T. . Quinto (Hrsg.), AMs - Contemporary Mathematics, S. 183-188, AMS - American Mathematical Society, 1990.
32. P. Maaß.
    Generalized Backus-Gilbert Methods.
    Inverse Methods in Action, P. Sabatier (Hrsg.), S. 440-446, Springer Verlag, 1990.

Zeitschriftenartikel (131)

1. P. Jansen, J. Le Clerc Arrastia, D. Otero Baguer, M. Schmidt, J. Landsberg, J. Wenzel, M. Emberger, D. Schadendorf, E. Hadaschik, P. Maaß, K. G. Griewank.
   Deep learning based histological classification of adnex tumors.
   European Journal of Cancer, 113431 196, 2024.

   DOI: 10.1016/j.ejca.2023.113431

2. J. G. Maaß, R. Herdt, L. Kinzel, M. Walther, H. Fröhlich, T. Schubert, C. Schaaf, P. Maaß.
   Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models.
   The Journal of the Acoustical Society of America, 156(4):2448-2466, 2024.

   DOI: 10.1121/10.0030473

3. D. Nganyu Tanyu, I. Michel, A. Rademacher, J. Kuhnert, P. Maaß.
   Parameter Identification by Deep Learning of a Material Model for Granular Media.
   GEM -- International Journal on Geomathematics, 15(13), 2024.

   DOI: 10.1007/s13137-024-00253-0
   online unter: https://arxiv.org/abs/2307.04166

4. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
   PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
   Inverse Problems, 39(6), 2023.

   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

5. D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
   Deep learning methods for partial differential equations and related parameter identification problems.
   Inverse Problems, 39(10), 2023.

   DOI: 10.1088/1361-6420/ace9d4

6. D. Erzmann, S. Dittmer, H. Harms, P. Maaß.
   DL4TO: A Deep Learning Library for Sample-Efficient Topology Optimization.
   Lecture Notes in Computer Science, Geometric Science of Information. GSI 2023 14071, Springer Verlag, 2023.

   DOI: 10.1007/978-3-031-38271-0_54

7. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

8. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
   Score-Based Generative Models for PET Image Reconstruction.
   Erscheint in Machine Learning for Biomedical Imaging

   online unter: https://arxiv.org/abs/2308.14190

9. R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin.
   An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
   IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.

   DOI: 10.1109/TCI.2022.3233188

10. S. Dittmer, C. Schönlieb, P. Maaß.
    Ground Truth Free Denoising by Optimal Transport.
    Erscheint in Numerical Algebra, Control, and Optimization

    online unter: https://arxiv.org/abs/2007.01575

11. C. Janßen, T. Boskamp, L. Hauberg-Lotte, J. Behrmann, S. Deininger, M. Kriegsmann, K. Kriegsmann, G. Steinbuß, H. Winter, T. Muley, R. Casadonte, J. Kriegsmann, P. Maaß.
    Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging.
    Proteomics - Clinical Applications, PRCA2208 , 2022.

    DOI: 10.1002/prca.202100068

12. A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
    Conditional Invertible Neural Networks for Medical Imaging .
    MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021.

    DOI: 10.3390/jimaging7110243

13. S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
    Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
    International Journal on Magnetic Particle Imaging, 7(1), 2021.

    online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/148

14. J. Le Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner , J. Schaller , P. Maaß.
    Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.
    MDPI Journal of Imaging, 71 7(4), Meisenbach Verlag, Bamberg, 2021.

    DOI: 10.3390/jimaging7040071

15. J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
    LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
    Scientific Data, 8(109), 2021.

    DOI: 10.1038/s41597-021-00893-z

16. M. Beckmann, P. Maaß, J. Nickel.
    Error analysis for filtered back projection reconstructions in Besov spaces.
    Inverse Problems, 37 014002 37(1), IOPscience, 2020.
17. T. Kluth, C. Bathke, M. Jiang, P. Maaß.
    Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
    Inverse Problems, 36(12), 2020.

    DOI: https://doi.org/10.1088/1361-6420/abc2fe

18. T. H. Nguyen, D. Nho Hào, P. Maaß, L. Colombi Ciacchi.
    Mathematical aspects of catalyst positioning in lithium/air batteries.
    Inverse Problems, 36(4), 2020.

    DOI: 10.1088/1361-6420/ab47e6

19. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
    Regularization by architecture: A deep prior approach for inverse problems.
    Journal of Mathematical Imaging and Vision, 62(3):456-470, Springer Verlag, 2020.

    DOI: 10.1007/s10851-019-00923-x
    online unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

20. T. Boskamp, D. Lachmund, R. Casadonte, L. Hauberg-Lotte, J. H. Kobarg, J. Kriegsmann, P. Maaß.
    Using the chemical noise background in MALDI mass spectrometry imaging for mass alignment and calibration.
    Analytical Chemistry, 92(1):1301-1308, 2020.

    DOI: 10.1021/acs.analchem.9b04473
    online unter: https://doi.org/10.1021/acs.analchem.9b04473

21. S. Dittmer, E. King, P. Maaß.
    Singular values for ReLU layers.
    IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.

    online unter: https://ieeexplore.ieee.org/document/8891761

22. S. Arridge, P. Maaß, O. Öktem, C. Schönlieb.
    Solving inverse problems using data-driven models.
    Acta Numerica, 28:pp. 1-174, Cambridge University Press, 2019.

    DOI: 10.1017/S0962492919000059

23. J. Quanico, L. Hauberg-Lotte, S. Devaux, Z. Laouby, C. Mériaux, A. Raffo-Romero, M. Rose, L. . Westerheide, J. Vehmeyer, F. Rodet, P. Maaß, D. Cizkova, N. Zilka, V. Cubinkova, I. Fournier, M. Salzet.
    3D MALDI mass spectrometry imaging reveals specific localization of long-chain acylcarnitines within a 10-day time window of spinal cord injury.
    Scientific Reports, 8(16083), 2018.

    DOI: 10.1038/s41598-018-34518-0

24. P. Fernsel, P. Maaß.
    A Survey on Surrogate Approaches to Non-negative Matrix Factorization.
    Vietnam Journal of Mathematics, 46(4):987-1021, Springer Verlag, 2018.

    DOI: 10.1007/s10013-018-0315-x

25. J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Learning for Tumor Classification in Imaging Mass Spectrometry.
    Bioinformatics, 34(7):1215-1223, Oxford University Press, 2018.

    DOI: 10.1093/bioinformatics/btx724

26. J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
    Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
    Bioinformatics, bty909 , 2018.

    DOI: 10.1093/bioinformatics/bty909

27. Y. Cordero Hernandez, T. Boskamp, R. Casadonte, L. Hauberg-Lotte, J. Oetjen, D. Lachmund, A. Peter, D. Trede, K. Kriegsmann, M. Kriegsmann, J. Kriegsmann, P. Maaß.
    Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer.
    Proteomics - Clinical Applications, 1700168 , Wiley, 2018.

    DOI: 10.1002/prca.201700168

28. N. Hase, S. M. Miller, P. Maaß, J. Notholt, M. Palm, T. Warneke.
    Atmospheric Inverse Modeling via Sparse Reconstruction.
    Geoscientific Model Development (GMD), 10:3695-3713, 2017.

    DOI: 10.5194/gmd-10-3695-2017

29. C. Bathke, T. Kluth, C. Brandt, P. Maaß.
    Improved image reconstruction in magnetic particle imaging using structural a priori information.
    International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.

    DOI: 10.18416/ijmpi.2017.1703015

30. T. Kluth, P. Maaß.
    Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction.
    International Journal on Magnetic Particle Imaging, Article ID 1707004 3(2), 10 pages, 2017.

    DOI: 10.18416/ijmpi.2017.1707004

31. T. Boskamp, D. Lachmund, J. Oetjen, Y. Hernandez-Cordero, D. Trede, P. Maaß, R. Casadonte, J. Kriegsmann, A. Warth, H. Dienemann, W. Weichert, M. Kriegsmann.
    A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.
    BBA - Proteins and Proteomics, , 2016.

    DOI: 10.1016/j.bbapap.2016.11.003

32. J. Oetjen, D. Lachmund, A. Palmer, F. Alexandrov, M. Becker, T. Boskamp, P. Maaß.
    An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments.
    Analytical and Bioanalytical Chemistry, 408(24):6729-40, 2016.

    DOI: 10.1007/s00216-016-9793-4

33. P. Maaß, R. Strehlow.
    An iterative regularization method for nonlinear problems based on Bregman projections.
    Inverse Problems, Article ID 115013 32(11), 20 pages, 2016.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/0266-5611/32/11/115013

34. Q. M. Pham, D. Nho Hào, P. Maaß, M. Pidcock.
    Descent Gradient Methods for Nonsmooth Minimization Problems and Applicatons to Ill-Posed Problems.
    Journal of Computational and Applied Mathematics, 298:105-122, 2016.

    DOI: 10.1016/j.cam.2015.11.039

35. F. Hoffmann, J. M. Lotz, J. Lotz, S. Heldmann, D. Trede, J. Oetjen, M. Becker, G. Ernst, P. Maaß, F. Alexandrov, O. Guntinas-Lichius, H. Thiele, F. von Eggeling.
    Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition.
    BBA - Proteins and Proteomics, , 2016.

    DOI: 10.1016/j.bbapap.2016.08.018

36. S. Devaux, D. Cizkova, J. Quanico, J. Franck, S. Nataf, L. Pays, L. Hauberg-Lotte, P. Maaß, J. H. Kobarg, F. Kobeissy, C. Mériaux, M. Wisztorski, L. Slovinska, J. Blasko, V. Cigankova, I. Fournier, M. Salzet.
    Proteomic Analysis of the Spatio-temporal Based Molecular Kinetics of Acute Spinal Cord Injury Identifies a Time- and Segment-specific Window for Effectife Tissue Repair.
    Molecular & Cellular Proteomics, 15:2641-2670, 2016.

    DOI: 10.1074/mcp.M115.057794

37. A. Egozi, D. Eilot, P. Maaß, C. Sagiv.
    A robust estimation method for camera calibration with known rotation.
    Applied Mathematics, 6(9):1538-1552 , 2015.

    DOI: 10.4236/am.2015.69137

38. J. Oetjen, K. Veselkov, J. Watrous, J. McKenzie, M. Becker, L. Hauberg-Lotte, J. H. Kobarg, A. K. Mróz, N. Strittmatter, F. Hoffmann, D. Trede, A. Palmer, S. Schiffler, K. Steinhorst, M. Aichler, R. Goldin, O. Guntinas-Lichius, F. von Eggeling, H. Thiele, K. Mädler, A. Walch, P. Maaß, P. C. Dorrestein, Z. Takats, F. Alexandrov.
    Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry.
    GigaScience, 4, 2015.

    DOI: 10.1186/s13742-015-0059-4

39. T. Dabrowski, A. Struck, D. Fenske, P. Maaß, L. Colombi Ciacchi.
    Optimization of Catalytically Active Sites Positioning in Porous Cathodes of Lithium/Air Batteries Filled with Different Electrolytes.
    Journal of The Electrochemical Society, 162(14):A2796-A2804, 2015.

    DOI: 10.1149/2.0861514jes

40. I. Piotrowska-Kurczewski, J. Vehmeyer, P. Gralla, F. E. Elsner-Dörge, F. Böhmermann, O. Riemer, P. Maaß.
    Reduzierung der Formabweichung beim Mikrofräsen.
    Tagungsband 7. Kolloquium Mikroproduktion, ISBN: 978-3-00-050-755-7 , 23–30 S., 2015.
41. H. Thiele, S. Heldmann, D. Trede, J. Strehlow, S. Wirtz, W. Dreher, J. Berger, J. Oetjen, J. H. Kobarg, B. Fischer, P. Maaß.
    2D and 3D MALDI-Imaging: Conceptual Strategies for Visualization and Data Mining.
    BBA - Proteins and Proteomics, 1844(1):117-137, 2014.

    DOI: 10.1016/j.bbapap.2013.01.040

42. B. Denkena, J. Vehmeyer, D. Niederwestberg, P. Maaß.
    Identification of the specific cutting force for geometrically defined cutting edges and varying cutting conditions.
    International Journal of Machine Tools and Manufacture, 82:42-49, 2014.

    DOI: 10.1016/j.ijmachtools.2014.03.009

43. J. H. Kobarg, P. Maaß, J. Oetjen, O. Tropp, E. Hirsch, C. Sagiv, M. Goldabaee, P. Vandergheynst.
    Numerical experiments with MALDI Imaging data.
    Advances in Computational Mathematics, 40(3):667-682, 2014.

    DOI: 10.1007/s10444-013-9325-0

44. M. Jiang, P. Maaß, T. Page.
    Regularizing properties of the Mumford-Shah functional for imaging applications.
    Inverse Problems, 30(3), 035007 , 2014.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/0266-5611/30/3/035007

45. P. Maaß, C. Sagiv, H. Stark, B. Torresani.
    Signal representation, uncertainty principles and localization measures.
    Advances in Computational Mathematics, 40(3):597-607, Springer Verlag, 2014.

    DOI: 10.1007/s10444-014-9341-8

46. M. Gehre, T. Kluth, C. Sebu, P. Maaß.
    Sparse 3D reconstructions in electrical impedance tomography using real data.
    Inverse Problems in Science and Engineering, 22(1):31-44, Taylor & Francis, 2014.

    DOI: 10.1080/17415977.2013.827183

47. A. Bartels, P. Dülk, D. Trede, F. Alexandrov, P. Maaß.
    Compressed sensing in imaging mass spectrometry.
    Inverse Problems, 29(12), 125015 (24pp), IOPscience, 2013.

    Selected as one of the highlights among all articles published in IOP "Inverse Problems" in 2013.

    DOI: 10.1088/0266-5611/29/12/125015

48. I. Piotrowska-Kurczewski, J. Schlasche, D. Weimer, B. Scholz-Reiter, P. Maaß.
    Image Denoising and Quality inspection of Micro Components using Perona-Malik Diffusion.
    Procedia CIRP, 8:432-437, Elsevier, 2013.

    DOI: 10.1016/j.procir.2013.06.129

49. J. Oetjen, M. Aichler, D. Trede, J. Strehlow, J. Berger, S. Heldmann, M. Becker, M. Gottschalk, J. H. Kobarg, S. Wirtz, S. Schiffler, H. Thiele, A. Walch, P. Maaß, F. Alexandrov.
    MRI-compatible pipeline for three-dimensional MALDI imaging mass spectrometry using PAXgene fixation .
    Journal of Proteomics, 90:52-60, 2013.

    DOI: 10.1016/j.jprot.2013.03.013

50. Q. M. Pham, D. Nho Hào, P. Maaß, M. Pidcock.
    Semismooth Newton and Quasi-Newton methods in weighted l¹-regularization.
    Journal of Inverse and Ill-posed Problems, 21(5):665-693, 2013.

    DOI: 10.1515/jip-2013-0031

51. B. Jin, T. Khan, P. Maaß.
    A reconstruction algorithm for electrical impedance tomography based on sparsity regularization .
    International Journal for Numerical Methods in Engineering, 89(3):337-353, 2012.

    DOI: 10.1002/nme.3247

52. F. Alexandrov, S. Bianconcini, E. B. Dagum, P. Maaß, T. S. McElroy.
    A review of some modern approaches to the problem of trend extraction.
    Econometric Reviews, 31(6):593-624, Taylor & Francis, 2012.

    DOI: 10.1080/07474938.2011.608032

53. S. Dahlke, U. Friedrich, P. Maaß, T. Raasch, R. Ressel.
    An adaptive wavelet solver for a nonlinear parameter identification problem for a parabolic differential equation with sparsity constraints.
    Journal of Inverse and Ill-posed Problems, 20(2):213-251, 2012.

    DOI: 10.1515/jip-2012-0013

54. B. Jin, P. Maaß.
    An analysis of electrical impedance tomography with applications to Tikhonov regularization.
    ESAIM: Control, Optimisation and Calculus of Variations, 18(4):1027-1048, 2012.

    DOI: 10.1051/cocv/2011193

55. D. Trede, S. Schiffler, M. Becker, S. Wirtz, K. Steinhorst, J. Strehlow, M. Aichler, J. H. Kobarg, J. Oetjen, A. Dyatlov, S. Heldmann, A. Walch, H. Thiele, P. Maaß, F. Alexandrov.
    Exploring Three-Dimensional Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry Data: Three-Dimensional Spatial Segmentation of Mouse Kidney.
    Analytical Chemistry, 84(14):6079-6087, 2012.

    DOI: 10.1021/ac300673y

56. D. Lorenz, P. Maaß, Q. M. Pham.
    Gradient descent for Tikhonov functionals with sparsity constraints: theory and numerical comparison of step size rules.
    Electronic Transactions on Numerical Analysis, 39:437-463, 2012.
57. D. Trede, F. Alexandrov, C. Sagiv, P. Maaß.
    Magnification of Label Maps with a Topology-Preserving Level-Set Method.
    IEEE Transactions on Image Processing, 21(9):4040-4053 , 2012.

    DOI: 10.1109/TIP.2012.2199325

58. C. Brandt, P. Maaß, I. Piotrowska-Kurczewski, S. Schiffler, O. Riemer, E. Brinksmeier.
    Mathematical methods for optimizing high precision cutting operations.
    International Journal of Nanomanufacturing, 8(4):306-325, 2012.

    DOI: 10.1504/IJNM.2012.048580

59. K. Kazimierski, P. Maaß, R. Strehlow.
    Norm sensitivity of sparsity regularization with respect to p.
    Inverse Problems, 104009 28(10), IOPscience, 2012.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/0266-5611/28/10/104009

60. D. Trede, J. H. Kobarg, J. Oetjen, H. Thiele, P. Maaß, F. Alexandrov.
    On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data.
    Journal of Integrative Bioinformatics, 9(1), 189 S., 2012.

    DOI: 10.2390/biecoll-jib-2012-189
    online unter: JIB open access

61. B. Jacob, B. Jin, T. Khan, P. Maaß.
    Optimal Source for Maximum Distinguishability in Optical Imaging.
    Journal of Applied Functional Analysis, 7(4):394-412, 2012.
62. M. Gehre, T. Kluth, A. Lipponen, B. Jin, A. Seppänen, J. P. Kaipio, P. Maaß.
    Sparsity Reconstruction in Electrical Impedance Tomography: An Experimental Evaluation.
    Journal of Computational and Applied Mathematics, 236(8):2126-2136, 2012.

    DOI: 10.1016/j.cam.2011.09.035

63. B. Jin, P. Maaß.
    Sparsity regularization for parameter identification problems.
    Inverse Problems, 123001 28(12), IOPscience, 2012.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/0266-5611/28/12/123001

64. B. Jin, T. Khan, P. Maaß, M. Pidcock.
    Function Spaces and Optimal Currents in Impedance Tomography.
    Journal of Inverse and Ill-posed Problems, 19(1):25-48, 2011.

    DOI: 10.1515/JIIP.2011.022, /May/2011

65. C. Brandt, J. Niebsch, R. Ramlau, P. Maaß.
    Modeling the Influence of Unbalances for Ultra-Precision Cutting Processes.
    ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 91(10):795-808, 2011.

    DOI: 10.1002/zamm.201000155

66. F. Alexandrov, S. Meding, D. Trede, J. H. Kobarg, B. Balluff, A. Walch, H. Thiele, P. Maaß.
    Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution.
    Journal of Proteomics, 75(1):237-245, Elsevier, 2011.

    DOI: 10.1016/j.jprot.2011.08.002

67. P. Maaß, N. Sochen.
    Uncertainty principles and localization measures.
    Operator Algebras and Representation Theory: Frames, Wavelets and Fractals, Oberwolfach Report 17:67-69, 2011.

    DOI: 10.4171/OWR/2011/17

68. P. Maaß.
    ZeTeM, Zentrum für Technomathematik, Universität Bremen, Germany.
    Newsletter of the European Mathematical Society, March 2011 79:36-38, 2011.

    DOI: 10.4171/NEWS

69. P. Maaß, M. Pidcock, C. Sebu.
    A regularized solution for the inverse conductivity problem using mollifiers.
    Inverse Problems in Science and Engineering, 18(1):145-161, 2010.

    DOI: 10.1080/17415970903234844

70. T. Bonesky, S. Dahlke, P. Maaß, T. Raasch.
    Adaptive wavelet methods and sparsity reconstruction for inverse heat conduction problems.
    Advances in Computational Mathematics, 33(4):385-411, Springer Verlag, 2010.

    DOI: 10.1007/s10444-010-9147-2

71. P. Maaß, C. Sagiv, N. Sochen, H. Stark.
    Do Uncertainty Minimizers Attain Minimal Uncertainty?
    Journal of Fourier Analysis and Applications, 16(3):448-469, Springer Verlag, 2010.

    DOI: 10.1007/s00041-009-9099-4

72. F. Alexandrov, M. Becker, S. Deininger, G. Ernst, L. Wehder, M. Grasmair, F. von Eggeling, H. Thiele, P. Maaß.
    Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering.
    Journal of Proteome Research, 9(12):6535-6546, 2010.

    DOI: 10.1021/pr100734z

73. K. Bredies, D. Lorenz, P. Maaß.
    A generalized conditional gradient method and its connection to an iterative shrinkage method.
    Computational Optimization and Applications, 42(2):173-193, Springer Verlag, 2009.

    DOI: 10.1007/s10589-007-9083-3

74. F. Alexandrov, J. Decker, B. Mertens, A. M. Deelder, H. Thiele, P. Maaß, R. A. E. M. Tollenaar.
    Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation.
    Bioinformatics, 25(5):643-649, 2009.

    DOI: 10.1093/bioinformatics/btn662

75. P. Maaß.
    Delay-range-dependent exponential H∞ synchronization of a class of delayed neural networks.
    Chaos, Solitons and Fractals, 41(3):1125-1135, Elsevier, 2009.

    DOI: 10.1016/j.mcm.2009.05.038

76. T. Bonesky, P. Maaß.
    Iterated Soft Shrinkage with Adaptive Operator Evaluations.
    Journal of Inverse and Ill-posed Problems, 17(4):337-358, 2009.

    DOI: 10.1515/JIIP.2009.023

77. C. Brandt, P. Maaß.
    Mathematical model of micro turning process.
    The International Journal of Advanced Manufacturing Technology, 45(1):33-40, Springer Verlag, 2009.

    DOI: 10.1007/s00170-009-1932-z

78. M. Lindemann, H. Thiele, P. Maaß, J. Decker.
    Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform.
    Computing and Visualization in Science, 12(4):189-199, Springer Verlag, 2009.

    DOI: 10.1007/s00791-008-0087-z

79. M. Lukaschewitsch, P. Maaß, M. Pidcock, C. Sebu.
    The asymptotic behaviour of weak solutions to the forward problem of electrical impedance tomography on unbounded three-dimensional domains.
    Mathematical Methods in the Applied Sciences, 32(2):206-222, WILEY-VCH, 2009.

    DOI: 10.1002/mma.1031

80. P. Maaß.
    A convex optimization approach to robust observer-based H∞ control design of linear parameter-varying delayed systems.
    International Journal of Modelling, Identification and Control, 4(3):226-241, 2008.

    DOI: 10.1504/IJMIC.2008.021160

81. M. Pidcock, P. Maaß, C. Sebu.
    A mollifier method for the inverse conductivity problem.
    Journal of Physics, Conference Series, Article ID 012068 135(1), 2008.

    DOI: 10.1088/1742-6596/135/1/012068

82. L. Prünte, P. Maaß, H. Thielemann.
    Condition Monitoring of linear guideways using a matched wavelet approach.
    Signal Processing, 88(7):1656-1670, Elsevier, 2008.

    DOI: 10.1016/j.sigpro.2007.12.017

83. P. Maaß, B. Kuhfuß, O. Riemer.
    Mathematical Models for surface characterization of machining processes.
    Microsystem Technologies, 14(12):1989-1993, Springer Verlag, 2008.

    DOI: 10.1007/s00542-008-0687-z

84. I. Loresch, H. Schulte, O. Riemer, P. Maaß, F. Dollinger.
    Mathematische Ansätze zur Materialabtragsmodellierung beim Polieren.
    HTM - Journal of Heat Treatment and Materials, 63(5):252-256, 2008.

    DOI: 10.3139/105.100472

85. T. Schuster, P. Maaß, T. Bonesky, K. Kazimierski, F. Schöpfer.
    Minimization of Tikhonov Functionals in Banach Spaces.
    Abstract and Applied Analysis, Article ID 192679, 2008, 18 pages, 2008.

    DOI: 10.1155/2008/192679

86. C. Brandt, J. Niebsch, R. Ramlau, A. Krause, O. Riemer, P. Maaß.
    Process Machine Interaction Model for Turning Processes.
    International Journal of Control Theory and Applications, 1(2):145-153, 2008.
87. S. Dahlke, D. Lorenz, P. Maaß, C. Sagiv, G. Teschke.
    The Canonical Coherent States Associated With Quotients of the Affine Weyl-Heisenberg Group.
    Journal of Applied Functional Analysis, 3(2):215-232, 2008.
88. S. Dahlke, G. Kutyniok, P. Maaß, C. Sagiv, H. Stark, G. Teschke.
    The uncertainty principle associated with the continuous shearlet transform.
    International Journal of Wavelets, Multiresolution and Information Processing, 6(2):157-181, 2008.

    DOI: 10.1142/S021969130800229X

89. K. Bredies, T. Bonesky, D. Lorenz, P. Maaß.
    A Generalized Conditional Gradient Method for Non-Linear Operator Equations with Sparsity Constraints.
    Inverse Problems, 23:2041-2058, 2007.
90. T. Bonesky, K. Bredies, D. Lorenz, P. Maaß.
    A generalized conditional gradient method for nonlinear operator equations with sparsity constraints.
    Inverse Problems, 23(5), 2007.

    DOI: 10.1088/0266-5611/23/5/014

91. P. Maaß, B. Kuhfuß, O. Riemer.
    Mathematische Modelle zur tribologischen Optimierung von Zerspanungsprozessen.
    Kolloquium Mikroproduktion - Fortschritte, Verfahren, Anwendungen, Kooperation der Sonderforschungsbereiche 440, 499, 516 und 747 :243-248, 2007.
92. E. Klann, D. Lorenz, P. Maaß, H. Thiele.
    Shrinkage versus Deconvolution.
    Inverse Problems, 23:2231-2248, 2007.

    DOI: 10.1088/0266-5611/23/5/025

93. K. Bredies, D. Lorenz, P. Maaß.
    An optimal control problem in medical image processing.
    Systems, Control, Modeling and Optimization, 202:249-259, Springer Verlag, 2006.

    DOI: 10.1007/0-387-33882-9_23

94. V. Dicken, P. Maaß, R. Ramlau, A. Rienaecker, C. Streller.
    Inverse Imbalance Reconstruction in Rotordynamics.
    ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 86(5):385-399, WILEY-VCH, 2006.

    DOI: 10.1002/zamm.200410248

95. E. Klann, P. Maaß, R. Ramlau.
    Two-step regularization methods for linear invrse problems.
    Journal of Inverse and Ill-posed Problems, 14(6):583-607, 2006.

    DOI: 10.1515/156939406778474523

96. K. Bredies, D. Lorenz, P. Maaß.
    Mathematical Concepts of Multiscale Smoothing.
    Applied and Computational Harmonic Analysis, 19(2):141-161, Elsevier, 2005.

    DOI: 10.1016/j.acha.2005.02.007

97. V. Dicken, P. Maaß, I. Menz, J. Niebsch, R. Ramlau.
    Nonlinear inverse unbalance reconstruction in rotor dynamics.
    Inverse Problems in Science and Engineering, 13(5):507-543, 2005.
98. S. Dahlke, P. Maaß, G. Teschke.
    Reconstruction of radar reflectivity densities in a narrowband regime.
    IEEE Transactions on Antennas and Propagation, 52(6):1603-1606, 2004.

    DOI: 10.1109/TAP.2004.829409

99. S. Dahlke, P. Maaß.
    A Note on Interpolating Scaling functions.
    Communications in Applied Analysis, 7(2):265-275, 2003.
100. S. Dahlke, P. Maaß, G. Teschke.
     Interpolating Scaling Functions with Duals.
     Journal of Computational Analysis and Application, 5(3):361-373, 2003.
101. P. Maaß, S. Dahlke, G. Teschke.
     Reconstructions of wideband reflectivity densities by wavelets transforms.
     Advances in Computational Mathematics, 18(2):189-209, Springer Verlag, 2003.

     DOI: 10.1023/A:1021303718373

102. M. Lukaschewitsch, P. Maaß, M. Pidcock.
     Tikhonov regularization for Electrical Impedance Tomography on unbounded domains.
     Inverse Problems, 19(3):585-610, 2003.

     DOI: 10.1088/0266-5611/19/3/308

103. T. Köhler, P. Maaß, P. Wust, M. Seebass.
     A fast Algorithm to find optimal controls of multiantenna applicators in regional hyperthermia.
     Physics in Medicine and Biology, 46:2503-2514, IOPscience, 2001.

     DOI: 10.1088/0031-9155/46/9/318

104. P. Maaß, S. V. Pereverzev, R. Ramlau, S. G. Solodky.
     An Adaptive Discretization for Tikhonov-Phillips Regularization with a Posteriori Parameter Selection.
     Numerische Mathematik, 87(3):485-502, 2001.

     DOI: 10.1007/PL00005421

105. D. Marpe, G. Blättermann, J. Ricke, P. Maaß.
     A Two-Layered Wavelet-Based Algorithm for Efficient Lossless and Lossy Image Compression.
     IEEE Transactions on Circuits and Systems for Video Technology, 10(7):1094-1102, 2000.

     DOI: 10.1109/76.875514

106. P. Maaß, G. Teschke, W. Willmann, G. Wollmann.
     Detection and Classification of Material Attributes - a practical application of Wavelet Analysis.
     IEEE Transactions of Signal Processing, 48(8):2432-2439, 2000.

     DOI: 10.1109/78.852022

107. S. Dahlke, K. Gröchenig, P. Maaß.
     A new approach to interpolating scaling functions.
     Applicable Analysis - An International Journal, 72(3):485-500, 1999.

     DOI: 10.1080/00036819908840755

108. A. Frommer, P. Maaß.
     Fast CG-Based Methods for Tikhonov--Phillips Regularization.
     SIAM Journal on Scientific Computing, 20(5):1831-1850, 1999.

     DOI: 10.1137/S1064827596313310

109. K. Dethloff, A. Weisheimer, A. Rinke, D. Handorf, M. V. Kurgansky, W. Jansen, P. Maaß, P. Hupfer.
     Climate variability in a nonlinear atmosphere-like dynamical system.
     Journal of Geophysical Research, 103:25957-25966, 1998.

     DOI: 10.1029/98JD02306

110. J. Ricke, P. Maaß, E. L. Hänninen, T. Liebig, H. Amthauer, C. Stroszczynski, W. Schauer, T. Boskamp, M. Wolf.
     Wavelet Versus JPEG (Joint Photographic Expert Group) and Fractal Compression: Impact on the Detection of Low-Contrast Details in Computed Radiographs.
     Investigative Radiology, 33(8):456-463, 1998.
111. S. Dahlke, P. Maaß.
     Interpolating Refinable Functions And Wavelets For General Scaling Matrices.
     Numerical Functional Analysis and Optimization, 18(5):521-539, Taylor & Francis, 1997.

     DOI: 10.1080/01630569708816776

112. S. Dahlke, P. Maaß.
     A Continuous Wavelet Transform on Tangent Bundles of Spheres.
     Journal of Fourier Analysis and Applications, 2(4):379-396, 1996.
113. P. Maaß, R. Ramlau.
     Accelerated iteration methods for inverse problems.
     ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, ICIAM '95 - Sonderband 76:183-186, 1996.
114. P. Maaß.
     Families of Orthogonal 2D Wavelets.
     SIAM Journal on Mathematical Analysis, 27(5):1454-1481, 1996.

     DOI: 10.1137/S003614109324649X

115. J. Ewert, P. Maaß.
     Local tomography in non-destructive testing.
     ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, ICIAM '95 - Sonderband 76:179-181, 1996.
116. P. Maaß, R. Ramlau.
     Wavelet-accelerated regularization methods for hyperthermia treatment planning.
     International Journal of Imaging Systems and Technology, 7(3):191-199, 1996.
117. V. Dicken, P. Maaß.
     Wavelet-Galerkin Methods for Ill-posed Problems.
     Journal of Inverse and Ill-posed Problems, 4(3):203-222, 1996.
118. S. Dahlke, P. Maaß.
     Continuous Wavelet Transforms with Applications to Analyzing Functions on Sheres.
     Journal of Fourier Analysis and Applications, 2(4):379-396, 1995.
119. S. Dahlke, P. Maaß.
     The Affine Uncertainty Principle in One and Two Dimensions.
     Computers & Mathematics with Applications, 30(3):293-305, 1995.

     DOI: 10.1016/0898-1221(95)00108-5

120. P. Maaß, H. Stark.
     Wavelets and digital image processing.
     Surveys on Mathematics for Industry, 4(3):195-235, 1994.
121. A. K. Louis, P. Maaß.
     Contour Reconstructions for 3D X-Ray CT-Images.
     IEEE Transactions on Medical Imaging, 12(4):764-769, 1993.
122. P. Maaß.
     The Interior Radon Transform.
     SIAM Journal on Applied Mathematics, 52(3):710-724, 1992.

     DOI: 10.1137/0152040

123. P. Maaß, W. Treimer, U. Feye-Treimer.
     Tomographic Methods for 2D Reconstructions with the Double Cristal Diffractometer.
     IMPACT of Computing in Science and Engineering, 4(3):250-268, Elsevier, 1992.

     DOI: 10.1016/0899-8248(92)90003-Q

124. W. Treimer, P. Maaß, B. Strothmann, U. Feye-Treimer.
     High-resolution neutron small-angle scattering with a double-crystal diffractometer and 2D reconstruction.
     Physica B: Condensed Matter, 174(1):532-536, Elsevier, 1991.

     DOI: 10.1016/0921-4526(91)90652-U

125. A. K. Louis, P. Maaß.
     Smoothed projection methods for the moment problem .
     Numerische Mathematik, 59(1):277-294, Springer Verlag, 1991.

     DOI: 10.1007/BF01385781

126. A. K. Louis, P. Maaß.
     A mollifier method for linear operator equations of the first kind.
     Inverse Problems, 6(3):427-440, IOPscience, 1990.

     DOI: 10.1088/0266-5611/6/3/011

127. P. Maaß.
     Tomographische Methoden bei Breitband Radar.
     ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 70(6):539-540, 1990.
128. P. Maaß.
     Wideband radar: the hyp transform .
     Inverse Problems, 5(5):849-857, IOPscience, 1989.

     DOI: 10.1088/0266-5611/5/5/012

129. P. Maaß.
     3D Röntgentomographie: Ein Auswahlkriterium für Abtastkurven.
     ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 68:498-499, 1988.
130. E. Hairer, P. Maaß.
     Numerical methods for singular nonlinear integro-differential equations .
     Applied Numerical Mathematics, 3(3):243-246, Elsevier, 1987.

     DOI: 10.1016/0168-9274(87)90051-1

131. P. Maaß.
     The x-ray transform: singular value decomposition and resolution.
     Inverse Problems, 3(4):729-741, IOPscience, 1987.

     DOI: 10.1088/0266-5611/3/4/016

Preprints (15)

1. R. Herdt, M. Schmidt, D. Otero Baguer, P. Maaß.
   Smooth Deep Saliency.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2404.02282

2. D. Nganyu Tanyu, J. Ning, A. Hauptmann, B. Jin, P. Maaß.
   Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2310.18636

3. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2302.02181

4. S. Dittmer, D. Erzmann, H. Harms, P. Maaß.
   SELTO: Sample-Efficient Learned Topology Optimization.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2209.05098

5. P. Fernsel, P. Maaß.
   Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2112.07641

6. S. Dittmer, P. Maaß.
   A Projectional Ansatz to Reconstruction.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/1907.04675

7. C. Etmann, M. Schmidt, J. Behrmann, T. Boskamp, L. Hauberg-Lotte, A. Peter, R. Casadonte, J. Kriegsmann, P. Maaß.
   Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/1912.05459

8. J. Behrmann, S. Dittmer, P. Fernsel, P. Maaß.
   Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/1806.09730

9. P. Maaß, Q. M. Pham.
   Semismooth Newton and Quasi-Newton Methods in Weighted l¹-Regularization of Nonlinear Inverse Problems.
   Berichte aus der Technomathematik 11-02, Universität Bremen, 2011.
10. P. Maaß, Q. M. Pham.
    Sparsity regularization for electrical impedance tomography: well-posedness and convergence rates.
    Berichte aus der Technomathematik 11-06, Universität Bremen, 2011.
11. P. Maaß, Q. M. Pham.
    Sparsity regularization of the diffusion coefficient problem: well-posedness and convergence rates.
    Berichte aus der Technomathematik 11-04, Universität Bremen, 2011.
12. B. Jin, T. Khan, P. Maaß.
    Sparse Reconstruction in Electrical Impedance tomography.
    Zur Veröffentlichung eingereicht.
13. V. Dicken, P. Maaß, I. Menz, J. Niebsch, R. Ramlau.
    Nonlinear Inverse Unbalance Reconstruction in Rotor dynamics.
    Berichte aus der Technomathematik 03-08, Universität Bremen, 2003.
14. T. Köhler, P. Maaß.
    Time-series forecasting for total volume data and charge back data.
    Berichte aus der Technomathematik 02-10, Universität Bremen, 2002.
15. S. Dahlke, P. Maaß, G. Teschke.
    Reconstruction of Reflectivity Desities by Wavelet Transforms.
    Berichte aus der Technomathematik 01-10, Universität Bremen, 2001.

Qualifikationsarbeiten (2)

1. P. Maaß.
   Die Konstruktion orthogonaler Wavelets und Anwendungen.
   Habilitationsschrift, Universität des Saarlandes, 1993.
2. P. Maaß.
   Die Singulärwertzerlegung der Röntgen-Transformation und ihre Anwendungen in der Computer-Tomographie.
   Dissertationsschrift, Technische Universität Berlin, 1988.

Tagungsbeiträge (39)

1. R. Herdt, M. Schmidt, D. Otero Baguer, J. Le Clerc Arrastia, P. Maaß.
   How GAN Generators can Invert Networks in Real-Time.
   The 15th Asian Conference on Machine Learning - ACML 2023, 11.11.-14.11.2023.
   PMLR, 222:422-437, 2024.

   online unter: https://proceedings.mlr.press/v222/herdt24a.html

2. R. Herdt, P. Maaß.
   Visualize and Paint GAN Activations.
   2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 22.09.-25.09.2024, London, Großbritannien.
   IEEE, 2024.

   DOI: 10.1109/MLSP58920.2024.10734757

3. M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
   SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
   Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.
   

   online unter: https://2023.midl.io/papers/p014

4. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
   Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
   ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
   

   online unter: https://invertibleworkshop.github.io/accepted_papers/index.html

5. T. Czotscher, D. Otero Baguer, F. Vollertsen, I. Piotrowska-Kurczewski, P. Maaß.
   Connection Between Shock Wave Induced Indentations And Hardness By Means Of Neural Networks.
   22nd International Conference on Material Forming (ESAFORM 2019), 08.05.-10.05.2019.
   AIP Conference Proceedings 2113, 100001, Springer Verlag, 2019.

   DOI: 10.1063/1.5112634

6. C. Etmann, S. Lunz, P. Maaß, C. Schönlieb.
   On the Connection Between Adversarial Robustness and Saliency Map Interpretability.
   36th International Conference on Machine Learning, 09.06.-15.06.2019, Los Angeles, USA.
   PMLR 97, 97:1823-1832, 2019.

   online unter: http://proceedings.mlr.press/v97/etmann19a.html

7. J. Flötotto, T. Kluth, M. Möddel, T. Knopp, P. Maaß.
   Improving Generalization Properties of Measured System Matrices by Using Regularized Total Least Squares Reconstruction in MPI.
   International Workshop on Magnetic Particle Imaging 2018.
   International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 53-54, Infinite Science Publishing, 2018.
8. D. Otero Baguer, P. Maaß.
   Inverse Problems in designing new structural materials.
   7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.
   

   DOI: 10.1007/978-3-030-55240-4_8

9. P. Gralla, D. . Rippel, M. Lütjen, P. Maaß.
   Inverting Prediction Models in Micro Production for Process Design.
   5TH INTERNATIONAL CONFERENCE ON NEW FORMING TECHNOLOGY, 18.09.-21.09.2018, Bremen, Deutschland.
   

   DOI: 10.1051/matecconf/201819015007

10. C. Bathke, T. Kluth, P. Maaß.
    MPI Reconstruction Using Structural Prior Information and Sparsity.
    International Workshop on Magnetic Particle Imaging 2018.
    International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 129-130, Infinite Science Publishing, 2018.
11. K. Tracht, A. Onken, P. Gralla, J. H. Emad, N. Kipry, P. Maaß.
    Trend-specific clustering for micro mass production of linked parts.
    CIRP General Assembly 2018, 19.08-25.08.2018.
    CIRP Annals, Manufacturing Technology, 67(1):9-12, Elsevier, 2018.

    DOI: 10.1016/j.cirp.2018.04.017

12. C. Bathke, T. Kluth, C. Brandt, P. Maaß.
    Improved image reconstruction in magnetic particle imaging using structural a priori information.
    International Workshop on Magnetic Particle Imaging 2017.
    International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, S. 85, Infinite Science Publishing, 2017.
13. T. Kluth, P. Maaß.
    Model uncertainty in magnetic particle iamging: Motivating nonlinear problems by model-based sparse reconstruction.
    International Workshop on Magnetic Particle Imaging 2017.
    International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, S. 83, Infinite Science Publishing, 2017.
14. P. Gralla, I. Piotrowska-Kurczewski, P. Maaß.
    Tikhonov Functionals Incorporating Tolerances.
    88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
    Erscheint in Proc. Appl. Math. Mech..
15. P. Gralla, I. Piotrowska-Kurczewski, P. Maaß.
    Parameter identification for micro milling processes using inverse problems incorporating tolerances.
    International Congress on engineering, design and Manufacturing 2016, 08.09-10.09.2016, Barcelona, Spanien.
    
16. T. Boskamp, D. Lachmund, J. Oetjen, Y. Hernandez-Cordero, J. Behrmann, J. H. Kobarg, R. Casadonte, J. Kriegsmann, P. Maaß.
    Visualizing MALDI TOF datasets of FFPE tissue samples for the purpose of quality assessment and comparison.
    OurCon IV - 2016, 17.10.-21.10.2016, Ustron, Polen.
    

    online unter: http://www.bioradint.eu/ourcon_public/papersview.php?showdetail=&paper_id=45

17. W. Zhang, G. Luo, L. Shen, T. Page, P. Li, M. Jiang, P. Maaß, J. Cong.
    FPGA acceleration by asynchronous parallelization for simultaneous image reconstruction and segmentation based on the Mumford-Shah regularization.
    SPIE OPTICAL ENGINEERING + APPLICATIONS, 09.08.-13.08.2015, San Diego, USA.
    Image Reconstruction from Incomplete Data VIII, Volume 9600, P. J. Bones, M. A. Fiddy, R. P. Millane (Hrsg.), SPIE, 2015.

    DOI: 10.1117/12.2187898

18. J. Vehmeyer, I. Piotrowska-Kurczewski, F. Böhmermann, O. Riemer, P. Maaß.
    Least-squares based parameter identification for a function-related surface optimisation in micro ball-end milling.
    15th CIRP Conference on Modelling Machining Operations (CIRP CMMO), 11.07.-12.07.2015, Karlsruhe, Deutschland.
    Procedia CIRP, 31:276-281, Elsevier, 2015.

    DOI: 10.1016/j.procir.2015.03.076

19. B. Denkena, A. Schmidt, P. Maaß, D. Niederwestberg, C. Niebuhr, J. Vehmeyer.
    Prediction of Temperature Induced Shape Deviations in dry Milling.
    15th CIRP Conference on Modelling Machining Operations (CIRP CMMO), 11.07.-12.07.2015, Karlsruhe, Deutschland.
    Procedia CIRP, 31:340-345, Elsevier, 2015.

    DOI: 10.1016/j.procir.2015.03.072
    online unter: http://www.sciencedirect.com/science/article/pii/S221282711500387X

20. P. Li, T. Page, G. Luo, W. Zhang, P. Zhang, P. Wang, P. Maaß, M. Jiang, J. Cong.
    FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation .
    2014 IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines , 11.05.-13.05.2014, Boston, MA, USA.
    

    DOI: 10.1109/FCCM.2014.54

21. A. Bartels, D. Trede, F. Alexandrov, P. Maaß.
    Hybrid Regularization and Sparse Reconstruction of Imaging Mass Spectrometry Data.
    10th International Conference on Sampling Theory and Applications (SampTA'13), 01.07.-05.07.2013, Bremen, Deutschland.
    

    online unter: http://www.eurasip.org/Proceedings/Ext/SampTA2013/papers/p189-bartels.pdf

22. I. Chernyavsky, F. Alexandrov, P. Maaß.
    A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data.
    German Conference on Bioinformatics 2012.
    

    DOI: 10.4230/OASIcs.GCB.2012.39

23. C. Brandt, J. Niebsch, P. Maaß, R. Ramlau.
    Simulation of Process Machine Interaction for Ultra Precision Turning.
    2nd International Conference on Process Machine Interactions, 10.06.-11.06.2010, Vancouver, Kanada.
    Proceedings of the 2nd International Conference on Process Machine Interactions, 2010.
24. C. Brandt, A. Krause, E. Brinksmeier, P. Maaß.
    Force modelling in diamond machining with regard to the surface generation process.
    9th International Conference and Exhibition on Laser Metrology, machine tool, CMM and robotic performance, 30.06.-02.07.2009, London, Großbritannien.
    Proceedings of the 9th International Conference and Exhibition on Laser Metrology, machine tool, CMM and robotic performance, LAMDAMAP 2009, S. 377-386, 2009.
25. A. Pehlken, A. Decker, P. Maaß, D. H. Müller, K. D. Thoben, S. Todt, M. Rolbiecki, W. Wosniok.
    Knowledge Based Recycling Concept.
    SETAC Europe 19th Annual Meeting, 31.05.2009 - 04.06.2009, Göteborg, Schweden.
    
26. A. Pehlken, A. Decker, P. Maaß, D. H. Müller, M. Rolbiecki, K. D. Thoben, S. Todt.
    LC thinking in combination with meta-modeling technique applied to recycling processes.
    4th International Conference on Life Cycle Management, 06.09.2009 - 09.09.2009, Kapstadt, Südafrika.
    
27. I. Loresch, H. Schulte, O. Riemer, E. Brinksmeier, P. Maaß, F. Dollinger.
    Mathematical Approaches for the Modelling of Material Removal in Polishing Processes.
    The 1st International Conference on NanoManufacturing, 13.07.-16.07.2008, Singapur, .
    Erscheint in Proceedings of the 1st International Conference on NanoManufacturing.
28. C. Brandt, P. Maaß.
    Model development for turning process.
    6th International Conference on Manufacturing Research, 09.09.-11.09.2008, Brunel University, West London, Großbritannien.
    Proceedings of the 6th International Conference on Manufacturing Research, S. 859-866, 2008.
29. C. Brandt, J. Niebsch, R. Ramlau, A. Krause, O. Riemer, P. Maaß.
    Process-Machine Interaction Model for Turning Process.
    The 1st International Conference on Process Machine Interactions, 03.09-04.09.2008, Leibniz Universität Hannover, Deutschland.
    Proceedings of the 1st International Conference on Process Machine Interactions, S. 239-246, 2008.
30. D. Lorenz, P. Maaß, H. Preckel, D. Trede.
    Topology-preserving geodesic active contours for segmentation of high-content fluorescent cellular imaging.
    79th Annual Meeting of the International Association of Applied Mathematics and Mechanics, 31.03-04.04.2008, Bremen, Deutschland.
    Proceedings in Applied Mathematics and Mechanics, 8(1):10941-10942, 2008.

    DOI: 10.1002/pamm.200810941

31. A. Decker, P. Maaß, D. H. Müller, A. Pehlken, M. Rolbiecki, K. D. Thoben, S. Todt, W. Wosniok.
    Unsicherheitsanalyse des Aufbereitungsprozesses am Beispiel Altreifenrecycling.
    Aufbereitung und Recycling 2008, 12.11.-13.11.2008, Freiberg, Deutschland.
    Erscheint in Tagungskompendium Aufbereitung und Recycling 2008.
32. K. Bredies, D. Lorenz, P. Maaß.
    An optimal control problem in image processing.
    PAMM.
    Proceedings in Applied Mathematics and Mechanics, 6(1):859-860, 2006.

    DOI: 10.1002/pamm200610409

33. T. Bonesky, K. Bredies, D. Lorenz, P. Maaß.
    On the minimization of non-convex, non-differentiable functionals with an application to SPECT.
    Oberwolfach Report: Mathematical Methods in Tomography.
    
34. R. Nordmann, D. Peters, B. Domes, P. Maaß.
    Inverse Determination of Imbalance Distributions.
    8th International Conference on Vibrations in Rotating Machinery, 07.09.-09.09.2004, University of Wales, England.
    
35. S. Dahlke, P. Maaß.
    An outline of adaptive wavelet galerkin methods for tikhonov regularization of inverse parabolic problems.
    International Conference on Inverse Problems, 09.01.-12.01.2002, Hong Kong, China.
    Recent development in Theories and Numerics, Y. Hon, M. Yamamoto, J. Cheng, J. Lee (Hrsg.), S. 56-66, World Scientific, 2002.

    DOI: 10.1142/9789812704924_0006

36. M. Quante, G. Teschke, M. Zhariy, P. Maaß, K. Sassen.
    Extraction and analysis of structural features in cloud radar and lidar data using wavelet based methods.
    2nd European conference on Radar Meteorology, ERAD, 18.11.-22.11.2002, Delft, Niederlande.
    ERAD Publication Series, H. Russchenberg (Hrsg.), 1:95-103, Copernicus Verlag, 2002.
37. V. Dicken, P. Maaß, B. Ott, M. Thierschmann.
    Eignung von Wavelet-Bilddatenkompressionsverfahren für Satellitenbildaufnahmen.
    Deutscher Luft- u. Raumfahrtkongress - DGLR Jahrestagung 1995, 26.09.-29.09.1995.
    
38. P. Maaß.
    Singular value decompositions for Radon transforms.
    Oberwolfach Report: Mathematical Methods in Tomography.
    

    DOI: 10.1007/BFb0084504

39. P. Gralla, I. Piotrowska-Kurczewski, D. . Rippel, M. Lütjen, P. Maaß.
    Eine Methode zur Invertierung von Vorhersagemodellen in der Mikrofertigung.
    8. Kolloquium Mikroproduktion, 27.11.-28.11.2017, Bremen, Deutschland.
    

Sonstiges (10)

1. C. Evers, K. Hachmann, M. Linden, A. Luttmann, K. Bredies, P. Maaß, F. Alexandrov.
   The Technomatika toolbox: LCMS peakpicking and alignment.
   Projektbericht, Universität Bremen, 2008.
2. L. Justen, P. Maaß.
   Verbesserte dynamische Festzieleliminierung beim Niederschlagsradar.
   Projektbericht, Universität Bremen, 2008.
3. S. Dahlke, P. Maaß.
   An Outline of Adaptive Wavelet Galerkin Methods for Tikhonov Regularization of Inverse Parabolic Problems.
   Berichte aus der Technomathematik 02-02, Universität Bremen, 2002.
4. V. Dicken, P. Maaß, I. Menz, J. Niebsch, R. Ramlau.
   Inverse Unwuchtidentifikation an Flugtriebwerken mit Quetschöldämpfern.
   Berichte aus der Technomathematik 02-09, Universität Bremen, 2002.
5. P. Maaß, T. Köhler, J. Kalden, L. R. Costa, U. Parlitz, C. Merkwirth, U. Wichard.
   Mathematical methods for forecasting bank transaction data.
   Projektbericht, Preprint Series DFG-SPP 1114, Preprint 24, 2002.
6. M. Lukaschewitsch, P. Maaß, M. Pidcock.
   Tikhonov regularization for Electrical Impedance Tomography on unbounded domains.
   Berichte aus der Technomathematik 02-08, Universität Bremen, 2002.
7. T. Köhler, P. Maaß, P. Wust, M. Seebass.
   Efficient methods in hyperthermia treatment planning.
   Berichte aus der Technomathematik 01-01, Universität Bremen, 2001.
8. S. Dahlke, P. Maaß.
   A Note on Interpolating Scaling Functions.
   Berichte aus der Technomathematik 00-13, Universität Bremen, 2000.
9. P. Maaß, G. Teschke, W. Willmann, G. Wollmann.
   Detection and Classification of Material Attributes -- A Practical Applicaion of Wavelet Analysis.
   Berichte aus der Technomathematik 00-10, Universität Bremen, 2000.
10. S. Dahlke, P. Maaß, G. Teschke.
    Interpolating Scaling Functions with Duals.
    Berichte aus der Technomathematik 00-08, Universität Bremen, 2000.