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Fast Patch Retrieval for Example-based Super Resolution by Multi-phase Candidate Reduction  

Park, Gyu-Ro (한동대학교 정보통신공학과)
Kim, In-Jung (한동대학교 전산전자공학부)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.4, 2010 , pp. 264-272 More about this Journal
Abstract
Example-based super resolution is a method to restore a high resolution image from low resolution images through training and retrieval of image patches. It is not only good in its performance but also available for a single frame low-resolution image. However, its time complexity is very high because it requires lots of comparisons to retrieve image patches in restoration process. In order to improve the restoration speed, an efficient patch retrieval algorithm is essential. In this paper, we applied various high-dimensional feature retrieval methods, available for the patch retrieval, to a practical example-based super resolution system and compared their speed. As well, we propose to apply the multi-phase candidate reduction approach to the patch retrieval process, which was successfully applied in character recognition fields but not used for the super resolution. In the experiments, LSH was the fastest among conventional methods. The multi-phase candidate reduction method, proposed in this paper, was even faster than LSH: For $1024{\times}1024$ images, it was 3.12 times faster than LSH.
Keywords
Example-based super resolution; Fast Patch Retrieval; Multi-phase Candidate Reduction; LSH;
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