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Proceedings Paper

Image super-resolution by combining the learning-based method and sparse-representation
Author(s): Qinlan Xie
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Paper Abstract

The learning-based method and sparse-representation of signal are combined to form the algorithm for single-image super-resolution. In the training phase, the correlation between the sparse-representation of high-resolution patches and that of low-resolution patches for the identical image with regard to their dictionaries is applied to train jointly two dictionaries for high- and low-resolution patches. In the super-resolution phase, the sparse-representation of each patch of low-resolution image is found to produce the high-resolution image by using corresponding coefficients of these representation and high-resolution patches obtained above. For the dictionary learned is a more compact representation of patches, the method demands less computational cost. Three experimentations validated the algorithm.

Paper Details

Date Published: 8 June 2012
PDF: 5 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83342W (8 June 2012); doi: 10.1117/12.956461
Show Author Affiliations
Qinlan Xie, Huazhong Univ. of Science and Technology (China)
South-Central Univ. of Nationalities (China)

Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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