Common CS Reconstruction Algorithms Including OMP

In computer science, signal reconstruction is a critical problem that involves recovering original signals from discrete or continuous signal samples. Compressed Sensing (CS) represents a novel signal reconstruction approach that not only reduces sampling rates but also enhances reconstruction accuracy. Unlike traditional sampling methods, CS algorithms demonstrate superior capability in handling noise and distortion within signals. Prominent CS reconstruction algorithms include Orthogonal Matching Pursuit (OMP) which iteratively selects the most correlated atoms from a dictionary, Stagewise OMP (SOMP) that extends OMP for simultaneous sparse approximation, Regularized OMP (ROMP) incorporating regularization constraints, Subspace AMP (SAMP) employing approximate message passing techniques, Compressive Sampling Matching Pursuit (CoSaMP) known for its robust performance guarantees, and Generalized PSM for Sparse Reconstruction (GPSR) utilizing gradient projection methods. Additionally, wavelet transforms implementing multi-resolution analysis through filter banks and Discrete Cosine Transform (DCT) employing frequency domain decomposition serve as fundamental signal reconstruction techniques widely applied in audio and video processing. Overall, signal reconstruction constitutes a broad field encompassing diverse algorithms and technologies designed to meet various application requirements.