针对压缩雾霾图像的复杂退化问题，提出频域引导的扩散修复框架。
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration
- 基于频域信息设计扩散框架，融合高频补偿增强细节恢复
- 在多个压缩雾霾数据集上优于当前最佳方法，提升显著
- 适合处理实际应用中带压缩伪影的雾霾图像修复
本研究揭示了雾霾退化与JPEG压缩之间的相互作用会引发复杂的联合损失效应，显著增加图像修复难度。现有去雾模型通常忽略压缩影响，限制了其在实际场景中的表现。为此，我们提出三项关键贡献：首先，设计了FDG-Diff，一种基于频域引导的新型去雾框架，通过利用频域信息提升JPEG图像的修复效果；其次，引入高频补偿模块（HFCM），将频域增强技术融入扩散修复框架，有效提升空间域细节恢复能力；最后，提出退化感知去噪时间步预测模块（DADTP），实现自适应区域化修复，缓解压缩雾霾图像中不同区域退化不一致的问题。在多个压缩去雾数据集上的实验表明，所提方法持续优于最新最先进水平。代码已开源：https://github.com/SYSUzrc/FDG-Diff。
原文摘要 · Abstract (English)
In this study, we reveal that the interaction between haze degradation and JPEG compression introduces complex joint loss effects, which significantly complicate image restoration. Existing dehazing models often neglect compression effects, which limits their effectiveness in practical applications. To address these challenges, we introduce three key contributions. First, we design FDG-Diff, a novel frequency-domain-guided dehazing framework that improves JPEG image restoration by leveraging frequency-domain information. Second, we introduce the High-Frequency Compensation Module (HFCM), which enhances spatial-domain detail restoration by incorporating frequency-domain augmentation techniques into a diffusion-based restoration framework. Lastly, the introduction of the Degradation-Aware Denoising Timestep Predictor (DADTP) module further enhances restoration quality by enabling adaptive region-specific restoration, effectively addressing regional degradation inconsistencies in compressed hazy images. Experimental results across multiple compressed dehazing datasets demonstrate that our method consistently outperforms the latest state-of-the-art approaches. Code be available at https://github.com/SYSUzrc/FDG-Diff.
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