Abstract

Existing image inpainting algorithm based on low-rank matrix approximation cannot be suitable for complex, large-scale, damaged texture image. An inpainting algorithm based on low-rank approximation and texture direction is proposed in the paper. At first, we decompose the image using low-rank approximation method. Then the area to be repaired is interpolated by level set algorithm, and we can reconstruct a new image by the boundary values of level set. In order to obtain a better restoration effect, we make iteration for low-rank decomposition and level set interpolation. Taking into account the impact of texture direction, we segment the texture and make low-rank decomposition at texture direction. Experimental results show that the new algorithm is suitable for texture recovery and maintaining the overall consistency of the structure, which can be used to repair large-scale damaged image.

Highlights

  • Image inpainting is a hot research for computer graphics and computer vision

  • Low-rank matrix approximation is an important method of data acquisition and representation and is a very effective way to obtain low-rank structure of high-dimensional data

  • We propose a new image restoration algorithm based on matrix low-rank approximation

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Summary

Introduction

Image inpainting is a hot research for computer graphics and computer vision. The main purpose is to, in a way of not being noticed, repair the damaged image or remove the unwanted objects in the image. Considering the curvature of the contour in the diffusion process, Chan and Shen [3] extended the TV method and proposed curvature-driven diffusion (CDD) method. It suppresses large curvature contours by curvature. The low-rank approximate (LRA) is a technology which hunts for Mathematical Problems in Engineering large-scale matrix low-rank representation, which is a powerful method to seek potential useful information from largescale complex data. Low-rank matrix method in signal processing, image processing, computer vision, highdimensional data analysis, and other fields has important and successful application; this paper mainly studies its application in image restoration

Related Works
Matrix Low-Rank Decomposition
Image Inpainting Algorithm
Experiment
Conclusion
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