The author introduces the MAE-like framework to enhance nighttime images by utilizing severe augmentation during training to develop strong network priors resilient to real-world night haze effects.
Severe augmentation during training yields strong network priors for effective nighttime image dehazing.
Proposing a semi-supervised model for real-world nighttime dehazing with spatial-frequency awareness and realistic brightness constraint.