AE  >> Vol. 6 No. 3 (May 2016)

    基于结构稀疏的图像修复实验设计
    The Design of Experiment on Image Inpainting Using Structure Sparisity

  • 全文下载: PDF(657KB) HTML   XML   PP.120-127   DOI: 10.12677/AE.2016.63019  
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作者:  

胡 晨,于文静,张晓宇,方 成,杨尊涵,陈宁:华东理工大学电子与信息实验教学中心,上海

关键词:
图像修复结构稀疏稀疏表达Image Inpainting Structure Sparisity Sparisity Expression

摘要:

对于自然图像稀疏先验的大量研究工作,为利用图像的结构稀疏性实现图像的修复奠定了基础。为了使学生了解结构稀疏的概念,并将这一特征应用于图像修复,本文设计了基于结构稀疏的图像修复实验。通过本实验,旨在使学生了解结构稀疏性及图像块稀疏表达的概念及应用,通过图像块稀疏实现图像修复,比较结构稀疏实现图像修复与经典图像修复的不同。文中提供不同算法在修复过程中的实现方式的不同以及每种方法的优缺点,帮助学生更好理解算法的实现过程和图像修复的意义。

The investigation on the sparisity of natural image is the basis of image inpainting using structure sparisity of images. The experiment is designed to deepen students’ understanding about the concept of structure sparisity and the implementation to the image inpainting technology. In the experiment, the conception and implementation of structure sparisity and sparisity expression are introduced to achieve image inpainting by image patch sparisity. The experiments and comparisons with the classical algorithm are performed. In the paper, the process, virtue and shortage of inpainting in different inpainting algorithms are compared, which is helpful for students to understand the process and the meaning of image inpainting.

文章引用:
胡晨, 于文静, 张晓宇, 方成, 杨尊涵, 陈宁. 基于结构稀疏的图像修复实验设计[J]. 教育进展, 2016, 6(3): 120-127. http://dx.doi.org/10.12677/AE.2016.63019

参考文献

[1] Bertalmio, M., Sapiro, G., Caselles, V., et al. (2000) Image Inpainting. Proceedings of ACM SIGGRAPH, New Orleans, 23-28 July 2000, 417-424.
[2] Elad, M., Starck, J.L., Querre, P. and Donoho, D.L. (2005) Simultaneous Cartoon and Texture Image Inpainting Using Morphological Component Analysis. Applied and Computational Harmonic Analysis, 19, 340-358. http://dx.doi.org/10.1016/j.acha.2005.03.005
[3] Guleryuz, O.G. (2003) Nonlinear Approximation Based Image Recovery Using Adaptive Sparse Reconstructions. Proceedings of 2003 International Conference on Image Processing, Vol. 1, 713-716. http://dx.doi.org/10.1109/icip.2003.1247061
[4] Xu, Z.B. and Sun, J. (2010) Image Inpainting by Patch Propagation Using Patch Sparsity. IEEE Transactions on Image Processing, 19, 1153-1165. http://dx.doi.org/10.1109/TIP.2010.2042098
[5] Zhang, Q and Li, J.J. (2012) Examplar-Based Image Inpainting Using Color Distribution Analysis. Journal of Information of Science and Engineering, 28, 641-654.
[6] Fadili, M.J., Starck, J.L. and Murtagh, F. (2009) Inpainting and Zooming Using Sparse Representations. The Computer Journal, 52, 64-79. http://dx.doi.org/10.1093/comjnl/bxm055
[7] 张晴. 基于样本的数字图像修复技术研究[D]: [博士学位论文]. 上海: 华东理工大学.
[8] 孙剑. 基于视觉先验的图像处理模型与算法研究[D]: [硕士学位论文]. 西安: 西安交通大学.
[9] Criminisi, A., Pérez, P. and Toyama, K. (2004) Region Filling and Object Removal by Exemplar-Based Inpainting. IEEE Transactions on Image Processing, 13, 1200-1212. http://dx.doi.org/10.1109/TIP.2004.833105
[10] 张德丰. 数字图像处理(MATLAB版) [M]. 北京: 人民邮电出版社, 2009: 15-30.
[11] 王家文. MATLAB7.6图形图像处理[M]. 北京: 国防工业出版社, 2009: 21-32.
[12] 张德丰. MATLAB数字图像处理[M]. 北京: 机械工业出版社, 2012: 276-285.
[13] Renaud, R., Baroncini, V. and Gusmao, A.A., et al. (2005) Final Report from the Video Quality Experts Group on the Validation of the Objective Models of Video Quality Assessment. http://www.wqeg.org
[14] Wong, A. and Orchard, J. (2008) A Nonlocal-Means Approach to exemplar-Based Inpainting. Proceedings of 15th IEEE International Conference on Image Processing, San Diego, 12-15 October 2008, 2600-2603. http://dx.doi.org/10.1109/icip.2008.4712326
[15] Wu, J.Y. and Ruan, Q.Q. (2006) Object Removal by Cross Iso-photes Exemplar-Based Inpainting. Proceedings of 18th International Conference on Pattern Recognition, Hong Kong, 810-813.