Shrinkage Thresholding Algorithm

Shrinkage thresholding algorithms are iterative methods used to solve inverse problems, particularly those involving sparse signal or image reconstruction. Current research focuses on integrating these algorithms into deep learning frameworks, often "unfolding" them into deep neural networks (DNNs) like those based on ISTA or FISTA, to leverage the strengths of both approaches, improving speed and accuracy. These advancements are significantly impacting fields like image processing, compressive sensing, and remote sensing by enabling faster and more accurate reconstruction from incomplete or noisy data. The development of hybrid models combining learned and pre-computed parameters within these algorithms further enhances performance and flexibility.