张量投票在图像修复中的应用
Application of Tensor Voting in Image Inpainting
DOI: 10.12677/JISP.2017.61001, PDF, HTML, XML, 下载: 1,925  浏览: 4,741 
作者: 邵晓芳, 初晓军：海军航空工程学院青岛校区，山东 青岛
关键词: 图像修复；自适应尺度；张量投票；Image Inpainting； Adaptive Scale； Tensor Voting
摘要: 图像修复技术是计算机图形学和机器视觉中的研究热点之一，在文物保护、多余目标物体剔除、影视特技制作、图像缩放、图像的有损压缩、视频通信的错误隐匿等方面均有重大应用价值。在分类总结相关工作的基础上，介绍了张量投票方法在图像修复中的应用，并展示了该方法的实验处理效果。
Abstract: Image inpainting is one of research hotspots both in computer graphics and computer vision, which can be applied in historic preservation, unwanted objects elimination, film and television special effects production, image resizing, image compression and error concealment of video communication. This paper presents a summary of related works and introduces how to apply the tensor voting method in optical pre-processing. The algorithm’s flowchart and typical experimental result are demonstrated to show the completion characteristics of the algorithm.
文章引用：邵晓芳, 初晓军. 张量投票在图像修复中的应用[J]. 图像与信号处理, 2017, 6(1): 1-7. http://dx.doi.org/10.12677/JISP.2017.61001

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. https://doi.org/10.1145/344779.344972
[2] 邵晓芳, 姜茂仁, 王勇. 数字图像修复研究综述[C]//2011国际信息技术与应用论坛论文集(《计算机科学》).
[3] Jia, J.Y. and Tang, C.-K. (2004) Inference of Segmented Color and Texture Description by Tensor Voting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 771-786. https://doi.org/10.1109/TPAMI.2004.10
[4] 史文中, 田岩, 黄应等. 基于张量投票的激光扫描数据修复方法[J]. 系统工程与电子技术, 2009, 31(11): 2724- 2727.
[5] Feyzabadi, S. and Carpin, S. (2014) Risk-Aware Path Planning Using Hierarchical Constrained Markov Decision Processes. IEEE International Conference on Automation Science and Engineering (CASE), Taipei, 18-22 August 2014, 297-303.
[6] 许毅平. 基于高光谱图像多特征分析的目标提取研究[D]: [博士学位论文]. 武汉: 华中科技大学, 2008.
[7] 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.