Remote Sensing Image Super-Resolution Method Using Sparse Representation and Classified Texture Patches

A super-resolution method based on sparse representation and classified texture patches wasproposed,mainly using the priori knowledge and texture to reconstruct remote sensing images.First,extract image blocks for dictionary learning,the over-complete dictionary was learned from the highand low resolution remote sensing image blocks.Orthogonal match pursuit was used to calculate thesparse conefficients,then the coefficients were fixed,iterative method was used to update the diction-ary until the algorithm converges.Then,the training dictionary was used to reconstruct the remotesensing images.In this step,the image was divided into smooth patches and non-smooth patches,bicubic interpolation was used for smooth patches while sparse conefficients and over-complete diction-ary were used for non-smooth patches.Experiment shows that this method has a faster reconstructionspeed and can achieve satisfied super-resolution results in the visual effects and objective evaluation in-dicators.