Integrated single image super resolution based on sparse representation

This paper presents a new and efficient approach for single-image super-resolution based on sparse signal recovery. This approach uses a co-occurrence trained dictionary of image patches that obtained from a set of observed low- and high-resolution images. The linear combination of the dictionary patches can recover every patch, then each patch that used on the low-resolution image, can be recovered by the dictionary patches. Since the recovered patch is a linear combination of some patches, the noise of every patch, aggregated in the recovered patch, then we prefer a linear combination which is more sparse rather than other combinations. So the sparse representation of patches can filter the noise in the solution. Recently this approach has been used in single image super-resolution problem. These methods calculate the sparse representation of every patches separately and set it to the recovered high-resolution image. So the complexity of such methods are very high and for suitable solution the parameters of algorithm must be estimated, therefore, this process (recover all patch with an iterative algorithm and parameter estimation for each iterate) is very time consuming. This paper presents an integrated method for recovering a low-resolution image based on sparse representation of patches with one step and recover whole image together.