Comprehensive Collection of Compressed Sensing Algorithms

Compressed sensing technology has emerged as a significant frontier in contemporary scientific research. Over recent years, researchers have conducted extensive investigations and explorations in this field, developing numerous novel algorithms and techniques. These innovations include but are not limited to: sparse representation methods that utilize basis pursuit and matching pursuit algorithms, compressive sensing frameworks employing L1-minimization techniques, and Hadamard matrix-based sampling approaches. To facilitate deeper research and practical applications of these methodologies, researchers have developed comprehensive MATLAB implementations featuring key functions such as measurement matrix generation, optimization solvers, and reconstruction algorithms. These MATLAB programs typically include implementations of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), and various convex optimization techniques for signal recovery. Consequently, compressed sensing technology demonstrates broad applicability and promising development prospects across diverse domains including medical imaging, wireless communications, and signal processing systems.