  Nighttime image dehazing is a challenging task due to the presence of
multiple types of adverse degrading effects including glow, haze, blurry,
noise, color distortion, and so on. However, most previous studies mainly focus
on daytime image dehazing or partial degradations presented in nighttime hazy
scenes, which may lead to unsatisfactory restoration results. In this paper, we
propose an end-to-end transformer-based framework for nighttime haze removal,
called NightHazeFormer. Our proposed approach consists of two stages:
supervised pre-training and semi-supervised fine-tuning. During the
pre-training stage, we introduce two powerful priors into the transformer
decoder to generate the non-learnable prior queries, which guide the model to
extract specific degradations. For the fine-tuning, we combine the generated
pseudo ground truths with input real-world nighttime hazy images as paired
images and feed into the synthetic domain to fine-tune the pre-trained model.
This semi-supervised fine-tuning paradigm helps improve the generalization to
real domain. In addition, we also propose a large-scale synthetic dataset
called UNREAL-NH, to simulate the real-world nighttime haze scenarios
comprehensively. Extensive experiments on several synthetic and real-world
datasets demonstrate the superiority of our NightHazeFormer over
state-of-the-art nighttime haze removal methods in terms of both visually and
quantitatively.
