Fast Linearized Alternating Direction Minimization Algorithm with Adaptive Parameter Selection for Multiplicative Noise Removal

TL;DR

This paper introduces two fast, adaptive algorithms for multiplicative noise removal that automatically estimate regularization parameters and outperform existing methods in accuracy and efficiency.

Contribution

The paper presents novel linearized algorithms that simultaneously estimate regularization parameters and recover images, improving speed and accuracy in multiplicative noise removal.

Findings

Outperform state-of-the-art methods in PSNR

Reduce computational time significantly

Converge under certain conditions

Abstract

Owing to the edge preserving ability and low computational cost of the total variation (TV), variational models with the TV regularization have been widely investigated in the field of multiplicative noise removal. The key points of the successful application of these models lie in: the optimal selection of the regularization parameter which balances the data-fidelity term with the TV regularizer; the efficient algorithm to compute the solution. In this paper, we propose two fast algorithms based on the linearized technique, which are able to estimate the regularization parameter and recover the image simultaneously. In the iteration step of the proposed algorithms, the regularization parameter is adjusted by a special discrepancy function defined for multiplicative noise. The convergence properties of the proposed algorithms are proved under certain conditions, and numerical experiments demonstrate that the proposed algorithms overall outperform some state-of-the-art methods in the PSNR values and computational time.