Volume 1, Issue 1, January 2011 - DROPS - Schloss Dagstuhl

110 11051 – Sparse Representations and Efficient Sensing of Data
2 Table of Contents
Executive Summary
Stephan Dahlke, Michael Elad, Yonina Eldar, Gitta Kutyniok, and Gerd Teschke . 108
Overview of Talks
Multivariate Periodic Function Spaces
Ronny Bergmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Random tight frames and applications
Martin Ehler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Alternating Direction Optimization for Imaging Inverse Problems with Sparsity-
Inducing Regularization
Mario Figueiredo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Adaptive wavelet methods for inverse parabolic problems
Ulrich Friedrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
A Majorization-minimization algorithm for sparse factorization and some related
applications
Onur G. Guleryuz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Stable discretizations of the hyperbolic cross fast Fourier transform
Stefan Kunis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Optimally Sparse Image Approximations Using Compactly Supported Shearlets
Wang-Q Lim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Exact test instances for Basis Pursuit Denoising
Dirk Lorenz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Cosparse Analysis Modeling – Uniqueness and Algorithms
Sangnam Nam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Space Splittings and Schwarz-Southwell Iterations
Peter Oswald . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Sparse Approximation of Images by the Easy Path Wavelet Transform
Gerlind Plonka-Hoch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Quadrature errors, discrepancies and variational dithering
Gabriele Steidl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Compressive sensing and inverse problems
Gerd Teschke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Sampling in the Age of Sparsity
Martin Vetterli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Digital Shearlet Transform on Pseudo-Polar Grids
Xiaosheng Zhuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Discussion Groups and Further Challenges
Sampling and Compressed Sensing
Martin Vetterli and Hans-Georg Feichtinger . . . . . . . . . . . . . . . . . . . . . . 123