Sparsity-Adaptive Algorithms for Compressed Sensing
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In compressed sensing, knowledge of signal sparsity is typically required; however, in practical applications, the sparsity level of signals is often unknown, posing challenges for algorithm design. To address this problem, sparsity-adaptive algorithms can be implemented to iteratively estimate signal sparsity without prior knowledge. These algorithms typically involve iterative thresholding or greedy pursuit methods that dynamically adjust sparsity estimates during reconstruction. For implementation, one may utilize functions like adaptive matching pursuit or iterative hard thresholding with sparsity refinement loops. Such approaches significantly improve reconstruction accuracy for sparse signals while reducing computational complexity. Consequently, sparsity-adaptive algorithms represent a highly promising research direction in the field of compressed sensing.
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