Super Resolution of Quality Images through Sparse Representation
This paper addresses the problem of generating the super resolution (SR) image from a single low resolution input image. Image patches can be represented as a sparse linear combination of elements from an over-complete dictionary. The low resolution image is viewed as down sampled version of a high resolution image. We look for a sparse representation for each patch of the low resolution image, and then use the coefficients of this representation to generate high resolution. Theoretically the sparse representation can be correctly recovered from the down sampled signals. The low and high resolution image patches are mutually training two dictionaries. We can look for the similarity between low and high resolution image patch pair of sparse representations with respect to their own dictionaries. Hence the high resolution image patch is applied to sparse representation of a low resolution image patch. This approach is more compact representation of the patch pairs compared to previous approaches. The earlier approaches simply sample a large amount of image patch pairs. The effectiveness of sparsity prior is demonstrated for general image super resolution. In this case, our algorithm generates high resolution images that are competitive or even superior in quality to images produced by other similar SR methods. This algorithm is practically developed and tested and it is generating high resolution image patches. The results are compared and analyzed with other similar methods.
KeywordsSR sparse representation dictionary noise sparsity
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