Repairing Sparse Low-Rank Texture
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- 作者：Xiao Liang (21) (22)
Xiang Ren (22)
Zhengdong Zhang (22)
Yi Ma (22) (23) - 关键词：Low ; Rank and Sparse Matrix Recovery ; Texture Completion ; Image Repairing
- 刊名：Lecture Notes in Computer Science
- 出版年：2012
- 出版时间：2012
- 年：2012
- 卷：7576
- 期：1
- 页码：496-509
- 全文大小：4028KB
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Xiang Ren (22)
Zhengdong Zhang (22)
Yi Ma (22) (23)
21. Tsinghua University, China
22. Visual Computing Group, Microsoft Research Asia, China
23. Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA
文摘
In this paper, we show how to harness both low-rank and sparse structures in regular or near regular textures for image completion. Our method leverages the new convex optimization for low-rank and sparse signal recovery and can automatically correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Through experimental comparisons with existing image completion systems (such as Photoshop) our method demonstrate significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.