Abstract
Remote sensing images (RSIs) are frequently degraded by haze, fog, and thin clouds, which obscure surface reflectance and hinder downstream applications. This study presents the first systematic and unified survey of RSIs dehazing, integrating methodological evolution, benchmark assessment, and physical consistency analysis. We categorize existing approaches into a three-stage progression: from handcrafted physical priors, to data-driven deep restoration, and finally to hybrid physical-intelligent generation, and summarize more than 30 representative methods across CNNs, GANs, Transformers, and diffusion models. To provide a reliable empirical reference, we conduct large-scale quantitative experiments on five public datasets using 12 metrics, including PSNR, SSIM, CIEDE, LPIPS, FID, SAM, ERGAS, UIQI, QNR, NIQE, and HIST. Cross-domain comparison reveals that recent Transformer- and diffusion-based models improve SSIM by 12%~18% and reduce perceptual errors by 20%~35% on average, while hybrid physics-guided designs achieve higher radiometric stability. A dedicated physical radiometric consistency experiment further demonstrates that models with explicit transmission or airlight constraints reduce color bias by up to 27%. Based on these findings, we summarize open challenges: dynamic atmospheric modeling, multimodal fusion, lightweight deployment, data scarcity, and joint degradations, and outline promising research directions for future development of trustworthy, controllable, and efficient (TCE) dehazing systems. All reviewed resources, including source code, benchmark datasets, evaluation metrics, and reproduction configurations are publicly available at this https URL.
Abstract (translated)
遥感图像（RSIs）常受雾、霾及薄云降解，导致地表反射率被遮蔽并阻碍下游应用。本研究首次对RSIs去雾技术进行系统化综述，整合了方法演进、基准评估与物理一致性分析。我们将现有方法归纳为三阶段演进路径：从手工物理先验方法，到数据驱动的深度复原，再到物理与智能混合生成，并总结了涵盖CNN、GAN、Transformer及扩散模型的30余种代表性方法。为提供可靠的实证参考，我们在五个公开数据集上采用PSNR、SSIM、CIEDE、LPIPS、FID、SAM、ERGAS、UIQI、QNR、NIQE及HIST等12项指标开展大规模定量实验。跨域对比表明，近期基于Transformer与扩散的模型平均提升SSIM达12%~18%，并将感知误差降低20%~35%，而物理引导的混合设计则实现更高辐射稳定性。专项物理辐射一致性实验进一步证明，具有显式透射率或大气光约束的模型可将色偏降低高达27%。基于上述发现，我们总结了开放挑战：动态大气建模、多模态融合、轻量化部署、数据稀缺性及复合降解问题，并展望了构建可信、可控、高效（TCE）去雾系统的未来研究方向。所有评审资源（包括源码、基准数据集、评估指标及复现配置）已公开于此链接。
URL
https://arxiv.org/abs/2603.20289
