Sparse representation with morphologic regularizations for single image super-resolution

摘要

Due to the fact that natural images are inherently sparse in some domains, sparse representation has led to interesting results in image acquiring, representing, and compressing high-dimensional signals. Based on the experiences and learned priors in sparse domain from low and high resolution images, the typical ill-posed inverse problem of image super-resolution is effectively solved by the l_1-norm optimization techniques. However, how to reasonably combine the sparse representation theory and the feature of natural images is still a critical issue for performances improvements of image super-resolution algorithms. Considering the fact that the different morphologic features in natural images should be regularized by different constrains in sparse domain, in this paper we present a novel sparse representation algorithm with reasonable morphologic regularization for single image super-resolution. Extensive experimental results on various natural images validate the superiority of the proposed algorithm in terms of qualitative and quantitative performance.