Comparing ASM and Learning-Based Methods for Satellite Image Dehazing - Steven Christ Pinantyo Arwidarasto; Rahadianti, Laksmita (Faculty of Computer Science - Universitas Indonesia, 2025)
Recent advancements in optical satellite technologies have significantly improved image resolution, providing more detailed information about Earth's surface. However, atmospheric interference, such as haze, is still a major factor in image capture. The interference results in visibility degradation of the acquired images, hindering computer vision tasks. Numerous studies have proposed various methods to recover haze-affected regions in satellite images, highlighting the need for more effective solutions. Motivated by this, this paper compares different atmospheric dehazing methods, including Atmospheric Scattering Model (ASM)-based and deep learning-based. The results show that SRD is the best ASM-based method, with a PSNR value of 19.09 dB and an SSIM of 0.908. Among deep learning models, DW-GAN achieves the best restoration results with a PSNR value of 26.22 dB and an SSIM of 0.959. SRD offers faster inference times, but still suffers from residual haze and noticeable color degradation compared to DW-GAN. In contrast, DW-GAN provides a more complete haze removal at the cost of higher computational demands than ASM-based methods.