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Abstract We propose a new high-quality and efficient single-image upscaling
technique that extends existing example-based super-resolution
frameworks. In our approach we do not rely on an external example
database or use the whole input image as a source for example
patches. Instead, we follow a local self-similarity
assumption on natural images and extract patches from extremely
localized regions in the input image. This allows us to reduce
considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local-self similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel non-dyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the new filters are nearly-biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient and allow a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modifications, and its efficiency to perform real-time enhancement of low-resolution video standard into recent high-definition formats.
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Links:
Bibtex reference: @article{FreFat10,
author = {Freedman, Gilad and Fattal, Raanan},
title = {Image and Video Upscaling from Local Self-Examples},
journal = {ACM Trans. Graph.},
volume = {28},
number = {3},
year = {2010},
issn = {0730-0301},
pages = {1--10},
doi = {http://doi.acm.org/10.1145/1531326.1531328},
publisher = {ACM},
address = {New York, NY, USA},
}
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