2009/01/01 by Martin Ehler, Ehler, Martin, Simone Geisel +1
Computer Science · Engineering · #Frames #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #shrinkage #sparse approximation #variational problems
paper · doi:10.4230/dagsemproc.08492.5
openalex publication_date 2009/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Finding a sparse representation of a possibly noisy signal is a common problem in signal representation and processing. It can be modeled as a variational minimization with ell_ au-sparsity constraints for au<1. Applications whose computation time is crucial require fast algorithms for this minimization. However, there are no fast methods for finding the exact minimizer, and to circumvent this limitation, we consider minimization up to a constant factor. We verify that arbitrary shrinkage rules provide closed formulas for such minimizers, and we introduce a new shrinkage strategy, which is adapted to au<1.