Interactive super-resolution through neighbor embedding

Authors:
Jian Pu;Junping Zhang;Peihong Guo;Xiaoru Yuan
Affiliations:
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China;Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China;Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing, China
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Year:
2009

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Abstract

Learning based super-resolution can recover high resolution image with high quality However, building an interactive learning based super-resolution system for general images is extremely challenging In this paper, we proposed a novel GPU-based Interactive Super-Resolution system through Neighbor Embedding (ISRNE) Random projection tree (RPtree) with manifold sampling is employed to reduce the number of redundant image patches and balance the node size of the tree Significant performance improvement is achieved through the incorporation of a refined GPU-based brute force kNN search with a matrix-multiplication-like technique We demonstrate 200-300 times speedup of our proposed ISRNE system with experiments in both small size and large size images.