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 发布日期:2017-04-19 出版日期:2017-04-10
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:2017-04-19 Published:2017-04-10
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.

参考文献

[1] GUILLEMOT C, LE MEUR O. Image inpainting: overview and recent advances[J]. IEEE Signal Processing Magazine, 2014, 31(1): 127-144.
[2] CHAN T, SHEN J. Local inpainting models and TV inpainting[J]. SIAM Journal on Applied Mathematics, 2001, 62(3): 1019-1043.
[3] CHAN T F, SHEN J. Nontexture inpainting by curvature-driven diffusions[J]. Journal of Visual Communication and Image Representation, 2001, 12(4): 436-449.
[4] RAM I, ELAD M, COHEN I. Image processing using smooth ordering of its patches[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2764-2774.
[5] WONG A, ORCHARD J. A nonlocal-means approach to exemplar-based inpainting[C]//ICIP 2008: Proceedings of the 15th IEEE International Conference on Image Processing. Piscataway, NJ: IEEE, 2008: 2600-2603.
[6] CRIMINISI A, PEREZ P, TOYAMA K. Region filling and object removal by exemplar-based image inpainting[J]. IEEE Transactions on Image Processing, 2004, 13(9): 1200-1212.
[7] SHEN B, HU W, ZHANG Y, et al. Image inpainting via sparse representation[C]//ICASSP 2009: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, DC: IEEE Computer Society, 2009: 697-700.
[8] LI Z, HE H, YIN Z, et al. A color-gradient patch sparsity based image inpainting algorithm with structure coherence and neighborhood consistency[J]. Signal Processing, 2014, 99(6): 116-128.
[9] XU Z, SUN J. Image inpainting by patch propagation using patch sparsity[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1153-1165.
[10] MAIRAL J, BACH F, PONCE J, et al. Non-local sparse models for image restoration[C]//Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Piscataway, NJ: IEEE, 2009: 2272-2279.
[11] ZHANG J, ZHAO D, GAO W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351.
[12] XU J, ZHANG L, ZUO W, et al. Patch group based nonlocal self-similarity prior learning for image denoising[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE, 2015: 244-252.
[13] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[14] GOLDSTEIN T, OSHER S. The split Bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 323-343.
[15] JIN K H, YE J C. Annihilating filter-based low-rank Hankel matrix approach for image inpainting[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3498-3511.
[16] AFONSO M V, SANCHES J M R. Blind inpainting using l₀ and total variation regularization[J]. IEEE Transactions on Image Processing, 2015, 24(7): 2239-2253.

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	邓凯丽, 魏伟波, 潘振宽. 改进掩码自编码器的工业缺陷检测方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2595-2603.
[2]	黄巧玲, 郑伯川, 丁梓成, 吴泽东. 融合监督注意力模块和跨阶段特征融合的图像修复改进网络[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 572-579.
[3]	陈刚, 廖永为, 杨振国, 刘文印. 基于多特征融合的多尺度生成对抗网络图像修复算法[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 536-544.
[4]	沈万里, 张玉金, 胡万. 面向图像修复取证的U型特征金字塔网络[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 545-551.
[5]	包永春, 张建臣, 杜守信, 张军军. 基于非负矩阵分解与稀疏表示的多标签分类算法[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1375-1382.
[6]	裴春阳, 樊宽刚, 马政. 基于边缘保留分解和改进稀疏表示的医学图像融合[J]. 计算机应用, 2021, 41(7): 2092-2099.
[7]	任晓奎, 刘鹏飞, 陶志勇, 刘影, 白立春. 基于单快拍信号到达角估计算法的室内入侵检测[J]. 计算机应用, 2021, 41(4): 1153-1159.
[8]	王丽娟, 陈少敏, 尹明, 许跃颖, 郝志峰, 蔡瑞初, 温雯. 基于近邻图改进的块对角子空间聚类算法[J]. 计算机应用, 2021, 41(1): 36-42.
[9]	袁琳君, 蒋旻, 罗敦浪, 江佳俊, 郭嘉. 基于生成对抗网络的人像修复[J]. 计算机应用, 2020, 40(3): 842-846.
[10]	冯浪, 张玲, 张晓龙. 基于扩张卷积的图像修复[J]. 计算机应用, 2020, 40(3): 825-831.
[11]	王肖, 魏嘉旺, 袁玉波. 基于特征部位圆形域的人脸图像修复方法[J]. 计算机应用, 2020, 40(3): 847-853.
[12]	高彦彦, 李莉, 张晶, 贾英茜. 结合梯度投影稀疏重构和复数小波的图像重构[J]. 《计算机应用》唯一官方网站, 2020, 40(2): 486-490.
[13]	杨文霞, 王萌, 张亮. 基于密集连接块U-Net的语义人脸图像修复[J]. 计算机应用, 2020, 40(12): 3651-3657.
[14]	宗春梅, 张月琴, 曹建芳, 赵青杉. 基于深度先验及非局部相似性的压缩感知核磁共振成像[J]. 计算机应用, 2020, 40(10): 3054-3059.
[15]	吴宗骏, 吴炜, 杨晓敏, 刘凯, Gwanggil Jeon, 袁皓. 改进的基于稀疏表示的全色锐化算法[J]. 计算机应用, 2019, 39(2): 540-545.