Nonconvex Total Generalized Variation Model for Image Inpainting

Xinwu Liu

doi:10.15388/20-infor438

Abstract

It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting. For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties. This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details. To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm. Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors.

Highlights

The research of image inpainting is an important and challenging topic in image processing and computer vision
As for image inpainting, this paper aims to overcome the shortcomings of existing inpainting models, and constructs a novel nonconvex total generalized variation (TGV) (NTGV) regularization strategy
We evaluate the inpainting performance compared to several state-of-the-art convex counterparts, in terms of both visual quality and restoration accuracy

Summary

Introduction

The research of image inpainting is an important and challenging topic in image processing and computer vision. The objective of image inpainting is to reconstruct the missing or damaged portions of image. For solving this inverse problem, there have emerged numerous models based on the variational, partial differential equation (PDE), wavelet, as well as Bayesian methods. Considering inpainting in the transformed domain, the works (Chan et al, 2006, 2009) discussed the TV minimization wavelet domain models for image inpainting. Among these models, one of the remarkable

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Results

Conclusion