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A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observa- tions with total variation (TV) regularization. This algorithm arises from a new half-quadratic model ...
A Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensing
We consider solving minimization problems with L_1-regularization: min ||x||_1 + mu f(x) particularly for f(x) = (1/2)||Ax-b||M2, where A is m by n and m < n. Our goal is to construct efficient and robust algorithms for ...
A Comparison of Three Total Variation Based Texture Extraction Models
This paper qualitatively compares three recently proposed models for signal/image texture extraction based on total variation minimization:the Meyer, Vese-Osher, and TV-L1 models. We formulate discrete versions of these ...