文献类型：期刊文献

英文题名：Structure-aware image inpainting using patch scale optimization

作者：Chen, Zhihua[1];Dai, Chao[1];Jiang, Lei[1];Sheng, Bin[2];Zhang, Jing[1];Lin, Weiyao[2];Yuan, Yubo[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China

年份：2016

卷号：40

起止页码：312

外文期刊名：JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

收录：;EI(收录号：20163102658011);WOS:【SCI-EXPANDED(收录号:WOS:000384398500027)】；

基金：The authors would like to thank all reviewers for their helpful suggestions and constructive comments. The work is supported by the National Natural Science Foundation of China (Nos. 61572316, 61133009, 61370714, 61402174), National High-tech R&D Program of China (863 Program) (Grant No. 2015AA015904), the Science and Technology Commission of Shanghai Municipality Program (No. 13511505000), the Interdisciplinary Program of Shanghai Jiao Tong University (No. 14JCY10).

语种：英文

外文关键词：Image inpainting; Texture synthesis; Local structure multiplier; Patch scale optimization

摘要：Image inpainting is widely used in many image processing applications such as image stitching, image editing and object removal. The main challenge stems from producing visually plausible results after reconstruction. Most of the image inpainting algorithms cannot maintain structure continuity and texture consistency precisely. To address this problem, we propose a robust exemplar-based inpainting algorithm. Firstly, we present local structure multiplier to contain sufficient structure information in the priority function which ensures the structure continuity. Secondly, we combine color feature and space distance between two patches to search for the optimized patch to avoid texture inconsistency. At last, we calculate the average pixel difference between two patches under each candidate scale, we select the scale which the minimal average pixel difference is to be the optimized scale. We copy the target patch with the optimized patch. Extensive experiments show the effectiveness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.