Brightness-Aware Synthetic-to-Real Learning for Nighttime Hazy Image Enhancement

Nighttime hazy vision is severely limited by the presence of haze and multi-colored light sources. Different from the daytime image dehazing task which has been widely studied, less progress has been made in nighttime image dehazing. In this paper, through extensive analysis and experimentation, we find that game engine simulations offer strong real-world generalization but suffer from unrealistic brightness. To tackle this, we introduce a three-step, brightness-aware synthetic-to-real learning approach. First, we use supervised learning to train a spatial-frequency network (SFN) on synthetic data to produce pseudo-labels. With these pseudo-labels, we develop a semi-supervised dehazing model (SFN+) that minimizes domain discrepancy through a brightness consistency loss applied to local windows. Building on SFN+, we fine-tune the model for better vision using a relative brightness improvement strategy that accounts for color shifts from lighting and brightness shifts during enhancement (SFN++). Experiments on popular benchmark datasets confirm our method's superiority over state-of-the-art approaches.