Comparison of Several Reconstruction Algorithms for Compressed Sensing

This text discusses several compressed sensing reconstruction algorithms such as OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), and SP (Subspace Pursuit). While these algorithms cover most common scenarios, numerous other approaches exist for signal reconstruction. These include but are not limited to: sparsity-based representation algorithms, analysis-based methods, and learning-based techniques. In code implementations, these algorithms typically involve key functions for sparse approximation and iterative refinement. For instance, OMP iteratively selects the most correlated dictionary atoms while CoSaMP employs a pruning mechanism to maintain sparsity constraints. Algorithm performance can be optimized by adjusting parameters such as convergence tolerance, maximum iteration counts, and sparsity levels through configuration variables in the code. Therefore, when selecting compressed sensing algorithms, one should choose the most suitable algorithm and parameters based on specific application requirements—considering factors like signal sparsity, measurement matrix properties, and computational constraints—to achieve optimal signal reconstruction results. Code implementations often include benchmarking modules to quantitatively compare reconstruction accuracy and computational efficiency across different methods.