NightHaze: Nighttime Image Dehazing via Self-Prior Learning

TL;DR

NightHaze introduces a self-prior learning approach for nighttime image dehazing, utilizing severe augmentation with light effects and noise to learn robust priors, significantly improving haze removal and detail restoration.

Contribution

The paper proposes a novel severe augmentation strategy inspired by MAE for nighttime dehazing, incorporating light effects and noise, and introduces a self-refinement module to reduce artifacts.

Findings

Achieves state-of-the-art dehazing performance with 15.5% and 23.5% improvements on key metrics.

Effectively suppresses glow and reveals background details in nighttime images.

Severe augmentation enhances model robustness against real-world haze degradations.

Abstract

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.