Satellite image super-resolution reconstruction based on ACGAN and dual-channel dense residual network

Abstract

For solving the problem of simple information image super-resolution reconstruction, this paper proposes a method based on ACGAN and dual-channel dense residual networks. Firstly, the different information represented by the images is classified to form different types of datasets. Focusing on image feature information, ACGAN is used to learn image feature information to generate images with this kind of feature information in this type of dataset so as to achieve image enhancement. Secondly, the study designed and proposed a dual-channel dense residual network to train the enhanced image dataset and achieve image super-resolution reconstruction. Experiments show that the method proposed in this paper not only can obtain higher reconstruction image quality than other methods but also obtain higher PSNR and SSIM than others. From this point of view, the application of this technology will have a far-reaching impact on the research of image super-resolution reconstruction with simple and sparse feature information.