Abstract

The sparse-representation of image and iterative Back-Projection method are combined to form the algorithm for single-image super-resolution. In the training phase, the correlation between the sparserepresentation 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. In the post-processing step, the iterative Back-Projection technical is used to reduce the super-resolution errors. For the dictionary learned is a more compact representation of patches, the method demands less computational cost. Three experimentations validated the algorithm.

Paper Details