Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior

Fengling Wang

doi:10.1155/2020/9365405

Abstract

Digital restoration of image with missing data is a basic need for visual communication and industrial applications. In this paper, making full use of priors of low rank and nonlocal self-similarity a gradual reweighted regularization is proposed for matrix completion and image restoration. Sparsity-promoting regularization produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternation direction method of multipliers algorithm is developed to speed up iterative solving of the above problem. Image block classification further enhances the adaptivity of the proposed method. Experiments on simulated matrix and natural image show that the proposed method obtains better image restoration results, where most lost information is reorcovered and few artifacts are produced.

Highlights

In today’s information age, the rapid development of computer technology facilitates digital record of rich visual information in pictures and videos, etc
Considering the correlation between local image blocks, we propose an image restoration algorithm called as nonlocal gradual reweighted regularization method (NGRR), where the singular value thresholding driven by the gradual reweighted regularization provides highly sparser representation of image data
As a fundamental inverse problem in image processing and low-level vision, matrix completion and image restoration by filling in damaged area aim to reconstruct a plausible image from outside information of the damaged area

Summary

Introduction

In today’s information age, the rapid development of computer technology facilitates digital record of rich visual information in pictures and videos, etc. Image restoration technology has made great progress, and many advanced methods have been introduced based on a variety of optimization models, mainly including variational calculus and partial differential equations [4, 5, 8, 12], methods based on such priors as exemplar matching and synthesis [13,14,15], sparse representation and low rank approximation [16,17,18,19,20,21,22,23,24,25,26], etc. Image restoration methods based on variational calculus and partial differential equations propagate/diffuse local structure information from the external of missing areas to the internal based on smoothness prior [4].

Gradual Reweighted Regularization

Results and Analysis

Conclusion