Exploiting self-similarities for single frame super-resolution

Authors:
Chih-Yuan Yang;Jia-Bin Huang;Ming-Hsuan Yang
Affiliations:
Electrical Engineering and Computer Science, University of California at Merced, Merced, CA;Electrical Engineering and Computer Science, University of California at Merced, Merced, CA;Electrical Engineering and Computer Science, University of California at Merced, Merced, CA
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

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Abstract

We propose a super-resolution method that exploits selfsimilarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance.