Super-resolution Reconstruction of Remote Sensing Images Based on Joint Nonnegative Dictionary Learning

Abstract

Aiming at the super-resolution reconstruction of images,a super-resolution reconstruction algorithm for a single image based on joint nonnegative dictionary learning is proposed in this paper,and it is applied in the super-resolution reconstruction of remote sensing images.Using existing high-resolution images,high-resolution and low-resolution samples are obtained by preprocessing.Joint nonnegative dictionary training technology is proposed,and high-resolution dictionary and low-resolution dictionary are obtained by training high-resolution and low-resolution samples,respectively.Super-resolution remote sensing image is recovered by these dictionaries,and computation complexity is analyzed.Experimental results show that,compared with bicubic interpolation method,Joint Dictionary Training(JDT) algorithm and coupled dictionary training algorithm,the proposed algorithm requires lower computation cost to achieve better reconstruction effect.