What techniques are used for sparse signal processing?

Sparse signal processing exploits sparsity (few significant components) for efficiency and performance. Techniques: Matching pursuit and OMP (greedy selection of dictionary atoms), Basis pursuit/LASSO (L1-regularized optimization for sparse solutions), Iterative thresholding (shrinkage for sparse recovery), and Dictionary learning (learn optimal sparse representation). Implementation: Efficient algorithms like ADMM, proximal gradient methods. Applications: Compressed sensing recovery, sparse coding for features, image inpainting, and audio source separation. Trade-offs: Computational complexity vs optimality vs sparsity level. Modern deep learning also learns sparse representations.