Remote sensing image super‐resolution based on convolutional blind denoising adaptive dense connection

Abstract

The current super-resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory. Therefore, a convolution blind denoising adaptive dense connection SR (CBD-ADCSR) network for the remote sensing image is proposed in this paper. The whole model is divided into a convolution blind denoising (CBD) network for denoising and an ADCSR network for reconstruction. Firstly, the components of the network are given in detail and are analysed. Secondly, a data set making method is designed combining motion blur, defocusing blur and Gaussian noise, which is used to generate low-resolution image data sets with complex degradation for the model training. Finally, through the detailed comparative experiment, it is proved that the reconstruction effect of the CBD-ADCSR model is better than that of the most state-of-the-art algorithms in objective criteria. In addition, compared with the original ADCSR network, CBD-ADCSR has a stronger ability for noise suppression.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Pyramidal dense attention networks for single image super‐resolution

“…Single image super-resolution (SISR) is a low-level vision problem that recovers a high resolution (HR) image from a low resolution (LR) observation, which is an ill-posed problem because multiple HR images can be degraded to the same LR image. To address this issue, researchers have proposed many approaches, which can be divided into three subclasses: interpolation-based methods [1], reconstruction-based methods [2], and learning-based methods [3][4][5][6][7][8][9][10].…”