Entropy-Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA) for inverse problems in imaging.

ABSTRACT: The iterative shrinkage-thresholding algorithm (ISTA) is a classic optimization algorithm for solving ill-posed linear inverse problems. Recently, this algorithm has continued to improve, and the iterative weighted shrinkage-thresholding algorithm (IWSTA) is one of the improved versions with a more evident advantage over the ISTA. It processes features with different weights, making different features have different contributions. However, the weights of the existing IWSTA do not conform to the usual definition of weights: their sum is not 1, and they are distributed over an extensive range. These problems may make it challenging to interpret and analyze the weights, leading to inaccurate solution results. Therefore, this paper proposes a new IWSTA, namely, the entropy-regularized IWSTA (E

SUBMITTER: Ma L 

PROVIDER: S-EPMC11676579 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature