MATLAB Code for Compressed Sensing Image Processing

This document presents MATLAB code implementations for compressed sensing image processing, covering algorithms such as CoSaMP, GBP, IHT, OMP, BP, and SP. These algorithms are fundamental in signal processing for compressing images and reducing data transmission requirements. The implementations typically involve sparse signal reconstruction through iterative optimization techniques - for instance, OMP sequentially selects dictionary atoms that best correlate with the residual signal, while CoSaMP incorporates a pruning step to maintain sparsity constraints. By employing these algorithms, users can significantly reduce file sizes while preserving image quality, enabling faster and more efficient data transmission. The applications extend beyond image processing to include audio signal processing, communication systems, and other domains requiring signal compression and reconstruction. Key MATLAB functions commonly implemented include sparse representation initialization, measurement matrix operations, thresholding procedures, and residual error calculations within iterative recovery loops.