Primal-dual method to the minimized surface regularization for image restoration

TL;DR

This paper introduces a primal-dual algorithm for image restoration using minimized surface regularization, offering an efficient alternative to classical models with proven convergence and improved restoration quality.

Contribution

It presents a novel primal-dual approach for minimized surface regularization in image restoration, with convergence analysis and competitive performance.

Findings

The proposed method converges reliably.

It outperforms existing methods in CPU time.

It achieves higher quality restored images.

Abstract

We propose a new image restoration model based on the minimized surface regularization. The proposed model closely relates to the classical smoothing ROF model \cite{4}. We can reformulate the proposed model as a min-max problem and solve it using the primal dual method. Relying on the convex conjugate, the convergence of the algorithm is provided as well. Numerical implementations mainly emphasize the effectiveness of the proposed method by comparing it to other well-known methods in terms of the CPU time and restored quality