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