Super Resolution Reconstruction of Remote Sensing Images Based on Online Variational Bayesian Estimation

A single remote sensing image super-resolution reconstruction method based on online variational Bayes expectation maximization coupled dictionary learning is proposed in this study to improve the spatial resolution of low resolution remote sensing images. The method first establishes the probability distribution model of the dictionary atom and each parameter, divides it into local variables and global variables, and uses the Gibbs sampling method to update the current parameters with fixed other parameters to assign initial values to the variables. Then stochastic optimization method is used to optimize expectation maximization (EM) optimization for two kinds of variables. The posterior distribution of the dictionary atom is obtained by minimizing the Kullback-Leibler (KL) distance, and the dictionary size is derived non-parametrically. Finally, the image to be reconstructed is divided into smooth and texture patches by bilateral filter during reconstruction, the sparse reconstruction method is used for the texture part while the bicubic interpolation reconstruction is applied for the smooth part. Compared with the bilinear, the bicubic interpolation and the super-resolution reconstruction algorithm based on sparse representation, the average peak signal-to-noise ratios of the proposed method arc increased by 3.85, 2.11, 0.20 dB, respectively. And the average relative global dimensional synthesis errors (ERGASs) arc decreased by 0.61, 0.28, 0.01 dB, respectively. Experimental results show that this algorithm can provide more high-frequency detail information by adding more sample and parameter prior information, which has certain universality and strong noise robustness, and the reconstruction speed is faster.