Exploiting Self-Similarities for Single Frame Super-Resolution

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Authors Chih-Yuan Yang, Jia-Bin Huang, Ming-Hsuan Yang
Journal/Conference Name ACCV
Paper Category
Paper 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.
Date of publication 2010
Code Programming Language MATLAB

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