标题:PatchTable: Efficient Patch Queries for Large Datasets and Applications
作者:Barnes, Connelly; Zhang, Fang-Lue; Lou, Liming; Wu, Xian; Hu, Shi-Min
作者机构:[Barnes, Connelly; Lou, Liming] Univ Virginia, Charlottesville, VA 22903 USA.; [Zhang, Fang-Lue; Wu, Xian; Hu, Shi-Min] Tsinghua Univ, TNList, Beiji 更多
会议名称:ACM SIGGRAPH Conference
会议日期:AUG 09-13, 2015
来源:ACM TRANSACTIONS ON GRAPHICS
出版年:2015
卷:34
期:4
DOI:10.1145/2766934
关键词:Approximate nearest neighbor; patch-based synthesis
摘要:This paper presents a data structure that reduces approximate nearest neighbor query times for image patches in large datasets. Previous work in texture synthesis has demonstrated real-time synthesis from small exemplar textures. However, high performance has proved elusive for modern patch-based optimization techniques which frequently use many exemplar images in the tens of megapixels or above. Our new algorithm, PatchTable, offloads as much of the computation as possible to a pre-computation stage that takes modest time, so patch queries can be as efficient as possible. There are three key insights behind our algorithm: (1) a lookup table similar to locality sensitive hashing can be precomputed, and used to seed sufficiently good initial patch correspondences during querying, (2) missing entries in the table can be filled during precomputation with our fast Voronoi transform, and (3) the initially seeded correspondences can be improved with a precomputed k-nearest neighbors mapping. We show experimentally that this accelerates the patch query operation by up to 9x over k-coherence, up to 12x over TreeCANN, and up to 200x over PatchMatch. Our fast algorithm allows us to explore efficient and practical imaging and computational photography applications. We show results for artistic video stylization, light field super-resolution, and multi-image editing.
收录类别:CPCI-S;EI;SCOPUS;SCIE
WOS核心被引频次:10
Scopus被引频次:20
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947294329&doi=10.1145%2f2766934&partnerID=40&md5=b6d5a199fe46cfe53d1b53f040ed3878
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