The Reconstruction Analysis Based on Minimum Total Variation & Bregman Algorithm

Xin-Yu LIU, Gong-Liu YANG, Yi-Ding SUN, Ya-Jie CHEN, Wei-Li CHEN

Abstract


Image compressed sensing makes sparse signals information to reconstruct the optimal solution of original signals. The minimum total variation and Bregman algorithm transform constrained optimization problem of 1 l -norm into unconstrained optimization problems by increasing punishment item. Sparse images make the iterative process more simple and rapidness. In this paper, by studying several kinds of reconstruction algorithm for image compressed sensing, the convergences and the speeds of these algorithms are analyzed.

Keywords


Compressed Sensing, Minimum Total Variation, Bregman, Sparse Reconstruction


DOI
10.12783/dtcse/aice-ncs2016/5674

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