The Devil is in the Darkness: Diffusion-Based Nighttime Dehazing Anchored in Brightness Perception

• URL: http://arxiv.org/abs/2506.02395v1
• Date: Tue, 03 Jun 2025 03:21:13 GMT
• Title: The Devil is in the Darkness: Diffusion-Based Nighttime Dehazing Anchored in Brightness Perception
• Authors: Xiaofeng Cong, Yu-Xin Zhang, Haoran Wei, Yeying Jin, Junming Hou, Jie Gui, Jing Zhang, Dacheng Tao,
• Abstract summary: We introduce the Diffusion-Based Nighttime Dehazing framework, which excels in both data synthesis and lighting reconstruction.<n>We propose a restoration model that integrates a pre-trained diffusion model guided by a brightness perception network.<n>Experiments validate our dataset's utility and the model's superior performance in joint haze removal and brightness mapping.
• Score: 58.895000127068194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While nighttime image dehazing has been extensively studied, converting nighttime hazy images to daytime-equivalent brightness remains largely unaddressed. Existing methods face two critical limitations: (1) datasets overlook the brightness relationship between day and night, resulting in the brightness mapping being inconsistent with the real world during image synthesis; and (2) models do not explicitly incorporate daytime brightness knowledge, limiting their ability to reconstruct realistic lighting. To address these challenges, we introduce the Diffusion-Based Nighttime Dehazing (DiffND) framework, which excels in both data synthesis and lighting reconstruction. Our approach starts with a data synthesis pipeline that simulates severe distortions while enforcing brightness consistency between synthetic and real-world scenes, providing a strong foundation for learning night-to-day brightness mapping. Next, we propose a restoration model that integrates a pre-trained diffusion model guided by a brightness perception network. This design harnesses the diffusion model's generative ability while adapting it to nighttime dehazing through brightness-aware optimization. Experiments validate our dataset's utility and the model's superior performance in joint haze removal and brightness mapping.

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