MATLAB Implementation of Compressed Sensing Reconstruction Algorithm Collection

This article provides an in-depth exploration of a MATLAB-implemented compressed sensing reconstruction algorithm collection. The suite includes fundamental algorithms such as Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), Iterative Hard Thresholding (IHT), Iteratively Reweighted Least Squares (IRLS), Gradient-Based Pursuit (GBP), Subspace Pursuit (SP), and Regularized Orthogonal Matching Pursuit (ROMP). These algorithms find extensive applications in signal processing, image reconstruction, and machine learning domains. Each algorithm will be thoroughly examined with explanations of their underlying mathematical principles, practical application scopes, and comparative advantages/limitations. The implementation aspects will cover key MATLAB functions including sparse matrix operations, optimization techniques, and recovery performance metrics. Furthermore, we will discuss algorithm selection strategies based on specific application requirements and demonstrate practical optimization techniques for enhancing computational efficiency and reconstruction accuracy. Through this comprehensive guide, readers will gain profound understanding of compressed sensing algorithms, master their implementation nuances, and acquire skills to achieve superior research outcomes in related fields.