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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Interactive Super-Resolution through Neighbor Embedding
verfasst von : Jian Pu, Junping Zhang, Peihong Guo, Xiaoru Yuan
Erschienen in: Computer Vision – ACCV 2009
Verlag: Springer Berlin Heidelberg
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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
I
nteractive
S
uper-
R
esolution system through
N
eighbor
E
mbedding (
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.