  Masked autoencoder (MAE) shows that severe augmentation during training
produces robust representations for high-level tasks. This paper brings the
MAE-like framework to nighttime image enhancement, demonstrating that severe
augmentation during training produces strong network priors that are resilient
to real-world night haze degradations. We propose a novel nighttime image
dehazing method with self-prior learning. Our main novelty lies in the design
of severe augmentation, which allows our model to learn robust priors. Unlike
MAE that uses masking, we leverage two key challenging factors of nighttime
images as augmentation: light effects and noise. During training, we
intentionally degrade clear images by blending them with light effects as well
as by adding noise, and subsequently restore the clear images. This enables our
model to learn clear background priors. By increasing the noise values to
approach as high as the pixel intensity values of the glow and light effect
blended images, our augmentation becomes severe, resulting in stronger priors.
While our self-prior learning is considerably effective in suppressing glow and
revealing details of background scenes, in some cases, there are still some
undesired artifacts that remain, particularly in the forms of over-suppression.
To address these artifacts, we propose a self-refinement module based on the
semi-supervised teacher-student framework. Our NightHaze, especially our
MAE-like self-prior learning, shows that models trained with severe
augmentation effectively improve the visibility of input haze images,
approaching the clarity of clear nighttime images. Extensive experiments
demonstrate that our NightHaze achieves state-of-the-art performance,
outperforming existing nighttime image dehazing methods by a substantial margin
of 15.5% for MUSIQ and 23.5% for ClipIQA.
