Title :
An overview of inverse problem regularization using sparsity
Author :
Starck, J.L. ; Fadili, M.J.
Author_Institution :
Lab. AIM, Univ. Paris Diderot, Gif-sur-Yvette, France
Abstract :
Sparsity constraints are now very popular to regularize inverse problems. We review several approaches which have been proposed in the last ten years to solve inverse problems such as inpainting, deconvolution or blind source separation. We will focus especially on optimization methods based on iterative thresholding methods to derive the solution.
Keywords :
blind source separation; deconvolution; optimisation; blind source separation; deconvolution; inpainting; iterative thresholding methods; optimization methods; sparsity constraints; Blind source separation; Compressed sensing; Deconvolution; Dictionaries; Inverse problems; Iterative methods; Optimization methods; Sampling methods; Signal design; Wavelet transforms; Blind Source Separation; Compressed Sensing; Deconvolution; Sparsity; inpainting; iterative thresholding;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5414556
