Improving Optical Remote Sensing Image Quality Through Random Degradation and Adaptive Fusion Super-Resolution Networks

High resolution optical remote sensing images are the guarantee for remote sensing image analysis and application. However, many images suffer from blurring, distortion and low resolution due to camera hardware limitations and unstable image transmission. To address these challenges, we propose a Random Degradation and Adaptive Fusion-based super-resolution network (RDAF-GAN) for improving the clarity and detail of images. Specifically, unlike the traditional single degradation method, we design a comprehensive simulation model for remote sensing image degradation. It aims to generate low-resolution remote sensing images that are closer to the real scene. Subsequently, these generated low-resolution images are fed into RDAF-GAN for reconstruction to recover finer and more accurate image details. In addition, we propose an image fusion method based on local contrast. By adaptively adjusting the fusion weights, the perceived clarity and visual quality of the images are further enhanced. The experimental results validate that RDAF-GAN outperforms other state-of-the-art methods and consistently produces excellent results in a variety of situations.