Color image restoration with impulse noise based on fractional-order total variation and framelet

TL;DR

This paper introduces a novel image restoration method combining fractional-order total variation and framelet transform, solved via ADMM, demonstrating improved performance in removing impulse noise from images.

Contribution

It proposes a new nonconvex model for impulse noise removal using fractional derivatives and framelet transform, along with an efficient ADMM-based solution and convergence analysis.

Findings

The method effectively reduces impulse noise in images.

Experimental results show superior restoration quality.

The algorithm converges reliably in tests.

Abstract

Restore lost images due to noise and blurred is a burgeoning subject in image processing and despite the different algorithms on this subject, but the effort to improve is always considered. The definition of fractional derivatives in recent years has created a powerful tool for this purpose. In the present paper, using fractional-order total variation and framelet transform, the nonconvex model for image restoration with impulse noise problem is improved. Then by alternating direction method of multipliers (ADMM) and primal-dual problem, the proposed model is solved. The convergence of the proposed algorithm is studied. And the proposed algorithm is evaluated using different types of tests. The output results show the efficiency of proposed method.