A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint

Xiaobin Wu,Tan Gao,Ziyu Zhang,Hongsong Qu,Liangliang Zheng

doi:10.3390/rs13245126

Abstract

Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively.

Highlights

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We can get an intuitive comparison of MODIS data from Figure 11, and the red ellipses are used to mark the residual stripes in the images
The majority of image processing problems are ill-posed inverse problems that can be addressed with a suitable regularization model

Summary

Introduction

Academic Editors: Karen Egiazarian, Vladimir Lukin and Aleksandra Pizurica. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The non-uniform photoresponse of image detectors causes stripe noise with distinct directional and structural features. It will reduce the subjective quality of images and limit their subsequent application in many fields. The purpose of our research is to estimate potential prior components to separate the clear image from the degraded image

Objectives

Methods

Discussion

Conclusion