Compressive Sensing Reconstruction Algorithm Collection

This article presents a comprehensive collection of compressive sensing reconstruction algorithms, featuring CoSaMP, GBP, IHT, IRLS, MP, OMP, and SP. These algorithms address the fundamental problem in signal processing where high-quality signals can be acquired with reduced costs and lower power consumption through compressive sensing techniques. Each algorithm possesses distinct advantages and specific application domains: CoSaMP demonstrates excellent performance with large-scale measurements, while OMP offers computational efficiency with lower complexity. From an implementation perspective, these algorithms typically involve iterative optimization procedures - for instance, OMP utilizes orthogonal projection to update residual signals, and IHT employs hard thresholding operations to enforce sparsity constraints. We will delve into detailed discussions about each algorithm's strengths, limitations, and practical applications in signal processing scenarios, including code implementation considerations such as stopping criteria, parameter tuning, and computational complexity analysis.