Compressed Sensing Reconstruction Algorithms

In the domain of compressed sensing reconstruction, numerous algorithms have been developed to enhance recovery quality and reduce computational complexity. This section introduces several prominent algorithms supported by experimental results. These reconstruction methods are fundamentally based on sparse coding principles using overcomplete dictionaries. The core concept leverages signal sparsity by representing signals as linear combinations of basis vectors. Key algorithms implementing compressed sensing recovery include: - OMP (Orthogonal Matching Pursuit): Iteratively selects dictionary atoms most correlated with the residual signal - CoSaMP (Compressive Sampling Matching Pursuit): An iterative algorithm that identifies multiple significant components per iteration - SP (Subspace Pursuit): Similar to CoSaMP but with modified selection criteria Each algorithm demonstrates distinct advantages and limitations, making selection dependent on specific application requirements. We recommend thorough comparative analysis and performance evaluation of these algorithms in practical implementations to achieve optimal reconstruction results. Code implementation typically involves iterative optimization loops, residual calculations, and support set updates using matrix operations from numerical computing libraries.