Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) a greedy algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better performance than other algorithms. However, SAMP still suffers from being sensitive to step size selection at high sub-sampling ratios. To solve this problem, paper proposes constrained backtracking (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds constraints, effectively controls increment estimated different stages accurately estimates true support set images. Based relationship analysis between signal measurement, energy criterion also proposed as constraint. At same time, four-to-one rule improved extra Comprehensive experimental results demonstrate that CBMP yields further stability algorithms