Compressed Sensing MATLAB Code Implementation
Compressed sensing, also known as compressive sampling or sparse sampling, represents a revolutionary sampling theory that leverages signal sparsity characteristics. It acquires discrete signal samples through random sampling at rates significantly lower than Nyquist requirements, followed by perfect signal reconstruction using nonlinear recovery algorithms. Since its introduction, compressed sensing has captured widespread attention across academia and industry, with applications spanning information theory, image processing, geosciences, optics, microwave imaging, pattern recognition, wireless communications, atmospheric studies, and geological research. Recognized as one of the top 10 scientific breakthroughs of 2007 by Technology Review, this implementation demonstrates signal generation, compressed sampling, and reconstruction with performance comparison through MATLAB code.