一种自适应组稀疏表示的图像修复方法

doi:10.11896/j.issn.1002-137X.2018.08.049

计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 272-276.doi: 10.11896/j.issn.1002-137X.2018.08.049

一种自适应组稀疏表示的图像修复方法

甘玲¹, 赵福超², 杨梦²

重庆邮电大学计算机科学与技术学院重庆400065¹
重庆邮电大学软件工程学院重庆400065²

收稿日期:2017-06-05 出版日期:2018-08-29 发布日期:2018-08-29
作者简介:甘玲(1966-),女,教授,硕士生导师,主要研究方向为计算机图形图像等,E-mail:ganling@cqupt.edu.cn(通信作者); 赵福超(1989-),男,硕士,主要研究方向为数字图像处理、智能信息处理; 杨梦(1992-),女,硕士,主要研究方向为数字图像处理、智能信息处理。
基金资助:
本文受国家自然科学基金项目(61272195)资助。

Self-adaptive Group Sparse Representation Method for Image Inpainting

GAN Ling¹, ZHAO Fu-chao², YANG Meng²

College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China¹
School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China²

Received:2017-06-05 Online:2018-08-29 Published:2018-08-29

摘要/Abstract

摘要： 针对组稀疏表示图像修复方法采用固定大小的图像块,致使修复结果中存在纹理和结构清晰性较差的问题,提出一种基于自适应组稀疏表示的图像修复方法。由于自然图像中纹理和结构信息不同,为了与原方法固定图像块大小的组结构作区分,首先提出一种自适应选取样本图像块大小的方法来构造自适应的组结构;然后以组为单位对其进行奇异值分解,获得该图像块组的自适应学习字典,并利用分裂伯格曼迭代(Split Bregman Iteration)算法求解目标代价函数;最后通过调整组中的图像块数量和迭代次数对每个组的自适应字典和稀疏编码系数进行更新,以获取较好的修复效果。实验结果表明,该方法不仅在峰值信噪比和特征相似性度量上有所提高,同时也提高了修复效率。

关键词: 图像修复, 稀疏表示, 自适应学习字典, 自适应组

Abstract: This paper proposed an image inpainting algorithm based on self-adaptive group sparse representation to solve the problem that the texture and structure clarity are poor in the repair results.Due to the difference of texture and structure information in the natural images,in order to distinguish the group structure with thefixed image block size in original algorithm,firstly,a method is proposed to adaptively select the size of sample image patch to construct an adaptive group structure.Secondly,singular value decomposition is conducted in groups to obtain an adaptive learning dictionary of the image patch group,and the Split Bregman Iteration algorithm is used to solve the objective cost function.Finally,the adaptive dictionary and the sparse coding coefficient of each group are updated by adjusting the number of image patches and iterations in the group to get a better restoration effect.The experimental results show that this method not only improves the peak signal to noise ratio and feature similarity index of image,but also improves the repair efficiency.

Key words: Image inpainting, Self-adaptive group, Self-adaptive learning dictionary, Sparse representation

中图分类号:

TP391

甘玲, 赵福超, 杨梦. 一种自适应组稀疏表示的图像修复方法[J]. 计算机科学, 2018, 45(8): 272-276. https://doi.org/10.11896/j.issn.1002-137X.2018.08.049

GAN Ling, ZHAO Fu-chao, YANG Meng. Self-adaptive Group Sparse Representation Method for Image Inpainting[J]. Computer Science, 2018, 45(8): 272-276. https://doi.org/10.11896/j.issn.1002-137X.2018.08.049

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一种自适应组稀疏表示的图像修复方法

Self-adaptive Group Sparse Representation Method for Image Inpainting

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