  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.
