An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal

TL;DR

This paper introduces a novel nonconvex DC model and an inexact proximal majorization-minimization algorithm for effectively removing stripe noise from remote sensing images, improving image quality and analysis accuracy.

Contribution

It proposes a new nonconvex destriping model with a DC structure and an efficient algorithm with guaranteed convergence for remote sensing images.

Findings

The proposed model outperforms existing destriping methods.

The algorithm guarantees convergence with an implementable stopping criterion.

Numerical experiments validate the effectiveness of the approach.

Abstract

The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this paper, we propose a nonconvex model with a DC function (i.e., the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization-minimization algorithm with each inner subproblem solved by the alternating direction method of multipliers. It deserves mentioning that we design an implementable stopping criterion for the inner subproblem, while the convergence can still be guaranteed. Numerical experiments demonstrate the superiority of the proposed model and algorithm.