Image super-resolution by combining the learning-based method and sparse-representation
Qinlan Xie · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
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.