基于自适应相似组稀疏表示的图像修复算法

doi:10.11772/j.issn.1001-9081.2017.04.1169

计算机应用 ›› 2017, Vol. 37 ›› Issue (4): 1169-1173.DOI: 10.11772/j.issn.1001-9081.2017.04.1169

• 计算机视觉与虚拟现实 • 上一篇下一篇

基于自适应相似组稀疏表示的图像修复算法

林金勇¹, 邓德祥¹, 颜佳¹, 林晓英²

1. 武汉大学电子信息学院, 武汉 430072;
2. 北京邮电大学电子工程学院, 北京 100876

收稿日期:2016-09-27 修回日期:2016-11-20 出版日期:2017-04-10 发布日期:2017-04-19
通讯作者: 林金勇
作者简介:林金勇(1992-),男,福建厦门人,硕士研究生,主要研究方向:数字图像处理、机器学习;邓德祥(1961-),男,湖北荆州人,教授,硕士,主要研究方向:计算机视觉、目标跟踪;颜佳(1983-),男,湖北天门人,讲师,博士,主要研究方向:目标跟踪、图像质量评价;林晓英(1992-),女,福建泉州人,硕士研究生,主要研究方向:数字图像处理、数据挖掘。
基金资助:
国家自然科学基金资助项目（61501334）。

Self-adaptive group based sparse representation for image inpainting

LIN Jinyong¹, DENG Dexiang¹, YAN Jia¹, LIN Xiaoying²

1. School of Electronic Information, Wuhan University, Wuhan Hubei 430072, China;
2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2016-09-27 Revised:2016-11-20 Online:2017-04-10 Published:2017-04-19
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61501334).

摘要/Abstract

摘要： 针对图像修复结果中存在的结构连续性和纹理清晰性较差的问题，提出了一种基于自适应相似组的图像修复算法。区别于传统的以单一图像块或固定数目图像块作为修复单元的方法，该算法根据自然图像中纹理区和结构区的不同特点，自适应地选取不同数目的相似图像块，构造自适应相似组；然后以相似组作为基本单元，学习自适应字典，并构造基于稀疏表示的图像修复模型；最后，采用Split Bregman Iteration算法高效地求解目标代价函数。实验结果表明，与基于图像块的图像修复算法和图像块组稀疏表示（GSR）算法相比，该算法在峰值信噪比（PSNR）上平均提高了0.94~4.34 dB，在结构相似性指数（SSIM）上平均提高了0.0069~0.0345，同时，修复速度分别是对比算法的2.51倍和3.32倍。

关键词: 图像修复, 稀疏表示, 自适应相似组, 学习字典, 自适应性

Abstract: Focusing on the problem of object structure discontinuity and poor texture detail occurred in image inpainting, an inpainting algorithm based on self-adaptive group was proposed. Different from the traditional method which uses a single image block or a fixed number of image blocks as the repair unit, the proposed algorithm adaptively selects different number of similar image blocks according to the different characteristics of the texture area to construct self-adaptive group. A self-adaptive dictionary as well as a sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iteration. The experimental results show that compared with the patch-based inpainting algorithm and Group-based Sparse Representation (GSR) algorithm, the Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index are improved by 0. 94-4.34 dB and 0. 0069-0.0345 respectively; meanwhile, the proposed approach can obtain image inpainting speed-up of 2.51 and 3.32 respectively.

Key words: image inpainting, sparse representation, self-adaptive group, dictionary learning, adaptivity

中图分类号:

TN911.7

林金勇, 邓德祥, 颜佳, 林晓英. 基于自适应相似组稀疏表示的图像修复算法[J]. 计算机应用, 2017, 37(4): 1169-1173.

LIN Jinyong, DENG Dexiang, YAN Jia, LIN Xiaoying. Self-adaptive group based sparse representation for image inpainting[J]. Journal of Computer Applications, 2017, 37(4): 1169-1173.

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基于自适应相似组稀疏表示的图像修复算法

Self-adaptive group based sparse representation for image inpainting

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