Publications of AG Inverse Problems and Imaging

Articles (7)

1. D. Lorenz, A. Tillmann, M. E. Pfetsch.
   An infeasible-point subgradient method using adaptive approximate projections.
   Computational Optimization and Applications, 57(2):271-306, 2014.

   DOI: 10.1007/s10589-013-9602-3
   online at: http://arxiv.org/abs/1104.5351

2. D. Lorenz, B. Komander, M. Fischer, M. Petz, R. Tutsch.
   Data fusion of surface normals and point coordinates for deflectometric measurements.
   Journal of Sensors and Sensor Systems, 3:281-290, 2014.

   DOI: 10.5194/jsss-3-281-2014

3. D. Lorenz, J. Lellmann, C. Schönlieb, T. Valkonen.
   Imaging with Kantorovich-Rubinstein discrepancy.
   SIAM Journal on Imaging Sciences, 7(4):2833-2859, 2014.

   DOI: 10.1137/140975528
   online at: http://arxiv.org/abs/1407.0221

4. D. Lorenz, M. Matz, K. Schumacher, K. Hatlapatka, K. Baumann.
   Observer-independent quantification of insulin granule exocytosis and pre-exocytotic mobility by TIRF microscopy.
   Microscopy and Microanalysis, 20(1):206-218, 2014.

   DOI: 10.1017/S1431927613013767

5. E. Herrholz, D. Lorenz, G. Teschke, D. Trede.
   Sparsity and Compressed Sensing in Inverse Problems.
   Lecture Notes in Computational Science and Engineering, 102:365-379, Springer Verlag, 2014.

   DOI: 10.1007/978-3-319-08159-5_18

6. D. Lorenz, C. Kruschel, J. S. Jørgensen.
   Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized x-ray CT.
   Inverse Problems in Science and Engineering, 23:1283-1305, 2014.

   DOI: 10.1080/17415977.2014.986724
   online at: http://arxiv.org/abs/1409.0214

7. D. Lorenz, S. Wenger, F. Schöpfer.
   The linearized Bregman method via split feasibility problems: Analysis and generalizations.
   SIAM Journal on Imaging Sciences, 2(7), 2014.

   DOI: 10.1137/130936269
   online at: http://arxiv.org/abs/1309.2094

Proceedings (1)

1. D. Lorenz, F. Schöpfer, S. Wenger, M. Magnor.
   sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing.
   IEEE International Conference on Image Processing.
   

   Recognized as one of the “Top 10%” papers

   DOI: 10.1109/ICIP.2014.7025269
   online at: http://arxiv.org/abs/1403.7543

Book Chapters (1)

1. E. Herrholz, D. Lorenz, G. Teschke, D. Trede.
   Sparsity and compressed sensing in inverse problems.
   Extraction of Quantifiable Information from Complex Systems, S. Dahlke, W. Dahmen, M. Griebel, W. Hackbusch, K. Ritter, R. Schneider, C. Schwab, H. Yserentant (Eds.), Lecture Notes in Computational Science and Engineering, pp. 365-379, Springer Verlag, 2014.