Entropy Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA): An Application to CT Image Restoration

TL;DR

This paper introduces ERIWSTA, an entropy-regularized iterative algorithm that improves CT image restoration by adaptively weighting attributes, leading to faster convergence and higher accuracy.

Contribution

The paper proposes a novel entropy regularizer for IWSTA, enhancing attribute participation and improving CT image restoration performance.

Findings

Faster convergence compared to existing methods

Higher restoration accuracy in CT imaging

Effective attribute weighting through entropy regularization

Abstract

The iterative weighted shrinkage-thresholding algorithm (IWSTA) has shown superiority to the classic unweighted iterative shrinkage-thresholding algorithm (ISTA) for solving linear inverse problems, which address the attributes differently. This paper proposes a new entropy regularized IWSTA (ERIWSTA) that adds an entropy regularizer to the cost function to measure the uncertainty of the weights to stimulate attributes to participate in problem solving. Then, the weights are solved with a Lagrange multiplier method to obtain a simple iterative update. The weights can be explained as the probability of the contribution of an attribute to the problem solution. Experimental results on CT image restoration show that the proposed method has better performance in terms of convergence speed and restoration accuracy than the existing methods.