When Aware Haze Density Meets Diffusion Model for Synthetic-to-Real Dehazing

Abstract

Image dehazing is an important preliminary step for downstream vision tasks. Existing deep learning-based methods have limited generalization capabilities for real hazy images because they are trained on synthetic data and exhibit high domain-specific properties. This work proposes a new Diffusion Model for Synthetic-to-Real dehazing (DMSR) based on the haze-aware density. DMSR mainly comprises of a physics-based dehazing model and a Conditional Denoising Diffusion Model (CDDM)-based model. The coarse transmission map and coarse dehazing result estimated by the physics-based dehazing model serve as conditions for the subsequent CDDM-based model. In this process, the CDDM-based dehazing model progressively refines the coarse transmission map while generating the dehazing result, enabling the model to remove haze with accurate haze density information. Next, we propose a haze density-aware resampling strategy that incorporates the coarse dehazed result into the resampling process using the transmission map, thereby fully leveraging the diffusion model for heavy haze removal. Moreover, a new synthetic-to-real training strategy with the prior-based loss function and the memory loss function is applied to DMSR for improving generalization capabilities and narrowing the gap between the synthetic and real domains with low computational cost. Extensive experiments on various real datasets demonstrate the effectiveness and superiority of the proposed DMSR over state-of-the-art methods.