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 | ECE |
| 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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