This program is intended to foster research, learning and collaboration between distinguished researchers in the various areas of Image Analysis. There will be a number of short courses devoted to various topics in Image Analysis and Compressed Sensing. These courses are aimed at attracting graduate students and postdoctoral fellows and at helping define the research questions to be investigated during the program.

In addition, there will be research talks by the participants in the program.
Preliminary topics for the program:

1. L1 minimization and applications (including Total Variation minimization).
2. Compressed Sensing by variational regularization methods.
3. Proximal point methods and iterative methods for solving ill-posed inverse problems (including iterative Bregman methods, hierarchical decompositions, surrogate functionals).
4. Geometric processing (denoising of surfaces, non-rigid shape processing and analysis).
5. Optimal Transportation and Wasserstein Distance methods for registration and segmentation.
6. PDE methods for image processing.
7. Nonlocal methods (nonlocal means, nonlocal total variation, bilateral filtering).