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山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (3): 32-37.doi: 10.6040/j.issn.1671-9352.0.2016.445

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基于结构自动匹配的仿射相似破损图像修复

于文静,毕东旭,颜学峰*   

  1. 华东理工大学信息科学与工程学院, 上海 200237
  • 收稿日期:2016-09-18 出版日期:2017-03-20 发布日期:2017-03-20
  • 通讯作者: 颜学峰(1972— ),男,博士,教授,研究方向为过程控制的建模与优化. E-mail:xfyan@ecust.edu.cn E-mail:wenjingyu@ecust.edu.cn
  • 作者简介:于文静(1974— ),女,硕士,讲师,研究方向为图像处理.E-mail:wenjingyu@ecust.edu.cn

The inpainting of affine similarity damaged image based on structure automatic match

YU Wen-jing, BI Dong-xu, YAN Xue-feng*   

  1. School of informationScience and Engineer, East China University of Science and Technology, Shanghai 200237, China
  • Received:2016-09-18 Online:2017-03-20 Published:2017-03-20

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1085

被引次数

1

摘要： 基于图像结构稀疏性定义了图像的结构稀疏算子,利用稀疏算子实现原图像到结构图像的映射。根据仿射相似图像具有相同结构的偏移量成稀疏分布的特点,统计相同结构偏移量的分布特征,获得破损区域的最优匹配信息。实验结果表明,该算法可以实现结构的自动匹配,在仿射破损图像的修复中更有效。

关键词: 仿射相似, 结构稀疏算子, 自动匹配

Abstract: Based on the character of image structure sparsity, the image structure sparsity operator was defined which can map from original image to structure image. According to the property of the sparse distribution of the similar structure offsets in affine similarity image, this paper indicates the distribution character of similar structure offsets through scientific statistics, with the objective of adaptively winning the superior matching region to the broken region. Experimental results show that the algorithm can achieve the structure match adaptively and can complete the affine broken image effectively.

Key words: automatic match, structure sparsity operator, affine similarity

中图分类号: 

  • TP391
[1] BERTALMIO M, SAPIRO G, CASELLES V, et al. Image inpainting [C] // Proceedings of ACM SIGGRAPH. New York: ACM, 2000: 417-424.
[2] GULERYUZ O G. Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part I: theory[J]. IEEE Transactions on Image Processing, 2006, 15(3):539-554.
[3] FADILI M J, STARCK J, MURTAGH F. Inpainting and zooming using sparse representations[J]. The Computer Journal, 2009, 52(1):64-79.
[4] MAIRAL J, ELAD M, SAPIRO G. Sparse representation for color image restoration[J]. IEEE Transactions on Image Processing, 2008, 17(1):53-69.
[5] ELAD M, AHARON M. Image denoising via learned dictionaries and sparse representation[C] // Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2006: 895-900.
[6] SHEN Bin, HU Wei, ZHANG Yimin, et al. Image inpainting via sparse representation[C] // Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Washington: IEEE Computer Society, 2009:697-700.
[7] XU Zongben, SUN Jian. Image inpainting by patch propagation using patch sparsity [J]. IEEE Transactions on Image Processing, 2010, 19(5): 1153-1165.
[8] 吴亚东,张红英,吴斌.数字图像修复技术[M].北京:科学出版社,2010:87-88. WU Yadong, ZHANG Hongying, WU Bin. Digital image inpainting[M ].Beijing: Science Press, 2010:87-88.
[9] EFROS A A, LEUNG T K. Texture synthesis by non-parametric sampling[C] // Proceedings of the 17th IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society, 1999:1033-1038.
[10] 李志丹,和红杰,尹忠科,等.基于块结构稀疏度的自适应图像修复算法[J].电子学报,2013,41(3):549-554. LI Zhidan, HE Hongjie, YIN Zhongke, et al. Adaptive image inpainting algorithm based on patch structure sparsity[J].Acta Electronica Sinica, 2013, 41(3):549-554.
[11] 孙玉宝,韦志辉,肖亮.多形态稀疏性正则化的图像超分辨率算法[J].电子学报,2010,38(12):2898-2902. SUN Yubao, WEI Zhihui, XIAO Liang. Multimorphology sparsity regularized image super-resolution [J].Acta Electronica Sinica, 2010, 38(12):2898-2902.
[12] 张岩,孙正兴,姚伟.基于方向经验模型分解的图像修复方法[J]. 电子学报,2010,38(2):257-262. ZHANG Yan, SUN Zhengxing, YAO Wei. Image completion based on direction empirical mode decomposition[J]. Acta Electronica Sinica, 2010, 38(2):257-262.
[13] JONGIN S, SEUNGRYONG K, KWANGHOON S. Fast affine-invariant image matching based on global Bhattacharyya measure with adaptive tree[C] // Proceedings of 2015 IEEE International Conference on Image Processing. New York:IEEE, 2015: 3190 -3194.
[14] 任澍,唐向宏,康佳伦. 纹理和边缘特征相结合的图像修复算法[J]. 计算机辅助设计与图形学学报, 2013, 25(11):1682-1693. REN Shu, TANG Xianghong, KANG Jialun. An image inpainting algorithm combined with texture and edge features[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(11): 1682-1693.
[15] KOSCHAN A, ABIDI M.Detection and classification of edges in color images[J]. IEEE Signal Processing, 2005, 22(1):64-73.
[16] CANNDY J A. Computational approach to edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698.
[17] HE Kai, SUN J. Statistics of patch offsets for image completion[C] // Proceeding of the 12th European Conference on Computer Vision. Berlin: Springer, 2012:16-29.
[18] 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.
[19] WANG Zhou, BOVIK A C, SHEIKH H R. Image quality assessment:from error visibility to structual similarity[J].IEEE Transactions on Image Processing, 2004, 13(4):600-612.
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