Compressive Sensing MATLAB Implementation Demo

This document presents a MATLAB program demonstration of Compressive Sensing. Compressive Sensing is a signal processing technique that aims to reduce the number of data samples required by utilizing sparse representations. This technology has been widely applied in various fields including image processing, speech processing, and wireless communications. In this demonstration, I will showcase how to implement the Compressive Sensing algorithm using MATLAB and demonstrate its application in image compression and recovery. The implementation includes key components such as sparse basis selection (using wavelet transforms), measurement matrix generation (random Gaussian matrices), and recovery algorithms (L1-minimization using linear programming). The code demonstrates practical considerations like signal sparsity verification, measurement number optimization, and reconstruction quality evaluation using metrics like PSNR. I hope this demonstration helps readers better understand Compressive Sensing technology and provides insights for research and development in related fields.