NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer

Understand

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.