FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration

• URL: http://arxiv.org/abs/2501.12832v1
• Date: Wed, 22 Jan 2025 12:19:47 GMT
• Title: FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration
• Authors: Ruicheng Zhang, Kanghui Tian, Zeyu Zhang, Qixiang Liu, Zhi Jin,
• Abstract summary: Existing dehazing models often neglect compression effects, which limits their effectiveness in practical applications. We introduce FDG-Diff, a novel frequency-domain-guided dehazing framework. Second, we introduce the High-Frequency Compensation Module (HFCM), which enhances spatial-domain detail restoration. Third, we introduce the Degradation-Aware Denoising Timestep Predictor (DADTP), which addresses regional degradation inconsistencies in compressed hazy images.
• Score: 16.348272500121336
• License:
• Abstract: 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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