FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration
Journal:
arXiv
Published Date:
Jan 22, 2025
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.
