Satellite Image Dehazing Based on Dual Frequency Pass Networks

Remote sensing using satellite imagery has been actively researched, inducing various applications of computer vision. In this field, the quality of satellite images is very important in facilitating continuous Earth observation and environmental monitoring. However, even after undergoing various correction processes, satellite images inevitably contain haze and clouds. The presence of these haze and clouds introduces numerous challenges to the acquisition of high-quality satellite images. In this study, we present a novel dehazing method designed to enhance the quality of satellite images named dual frequency pass networks (DFPNs). The proposed method comprises two branches: a transformer branch for capturing low-frequency components and a convolution branch for extracting high-frequency components. Thus, this approach can consider both the global features from the transformer and the local features from the convolution. The experiments demonstrate that the proposed method outperforms other state-of-the-art methods.