Super-Resolution Algorithm of Satellite Cloud Image Based on WGAN-GP

Abstract

The resolution of an image is an important indicator for measuring image quality. The higher the resolution, the more detailed information is contained in the image, which is more conducive to subsequent image analysis and other tasks. Improving the resolution of images has always been the unremitting pursuit of industry and academia. In the past, people used hardware devices to increase the resolution, which is a practical solution. However, there are many limitations in the method of improving the image resolution by hardware devices. We use software-based image super-resolution technology, which transforms low-resolution images into high-resolution images through a series of machine learning algorithms. The classic GAN algorithm is difficult to train a model, and the improved Wasserstein GAN algorithm can make the model training more stable. Based on SRGAN model, this algorithm replaces the classical GAN algorithm with the improved WGAN algorithm. We will use the FY-3D satellite’s Medium Resolution Spectral Imager Type II (MERSI-II) data, using super-resolution algorithms to make the reconstructed image significantly better visually. We conducted four sets of controlled experiments using four different improved methods. We will evaluate the image from three aspects: peak signal to noise ratio value, structural similarity value and visual effect. We applied the WGAN-GP algorithm to super-resolution tasks and achieved the desired results.