Image super-resolution by combining the learning-based method and sparse-representation

Qinlan Xie

doi:10.1117/12.956461

8 June 2012 Image super-resolution by combining the learning-based method and sparse-representation

Qinlan Xie

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 83342W (2012) https://doi.org/10.1117/12.956461
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

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.

Citation Download Citation

Qinlan Xie "Image super-resolution by combining the learning-based method and sparse-representation", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83342W (8 June 2012); https://doi.org/10.1117/12.956461

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