A fast super-resolution method based on sparsity properties
Super-resolution enhancement is a kind of promising approach to enhance the spatial resolution of images. To super-resolve a satisfying result, regularization term design and blur kernel estimation are two important aspects which need to be carefully considered. In this paper, we propose a robust regularized super-resolution reconstruction approach based on two sparsity properties to deal with these two aspects. Firstly, we design a sparse reweighted TV L1 prior to restrict the first derivative of the upsampled image. Then, noticing that only deblurring sparse high gradient areas can sharpen the super-resolution result, we design an over-deblurring control method to decrease the artifacts caused by inaccurate blur kernel estimation. We also design a fast optimization algorithm to solve our model. The experimental results show that the proposed approach achieves a remarkable performance both in visual quality and run time.