Super-Resolution of Remote Sensing Imagery Using Implicit Degradation Modeling
Han Oh, Dong-Jin Kim, Sun Gu Lee, Daewon Chung · 2023
Super-resolution (SR) techniques are widely used in remote sensing image analysis to enhance image resolution. However, existing SR methods often struggle to accurately model the complex image degradation processes encountered in remote sensing images. In this paper, we propose a simple yet effective SR method specifically designed for remote sensing images. The proposed method incorporates a quality degradation modeling network and a SR network to bridge the gap between low-resolution (LR) and high-resolution (HR) images. Experimental results using Korea Multi-purpose Satellite (KOMPSAT)-3/3A and WorldView-3 images demonstrate that the proposed method significantly improves the quality of the source images by leveraging the high-resolution characteristics of the HR images.