NightCC: Nighttime Color Constancy via Adaptive Channel Masking

Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nighttime scenes, frequently leading to imprecise light color estimations. To tackle nighttime color constancy, we propose a novel unsupervised domain adaptation approach that utilizes labeled daytime data to facilitate learning on unlabeled nighttime images. To specifically address the unique lighting conditions of nighttime and ensure the robustness of pseudo labels, we propose adaptive channel masking and light uncertainty. By selectively masking channels that are less sensitive to lighting conditions, adaptive channel masking directs the model to progressively focus on features less affected by variations in light colors and noise. Additionally, our model leverages light uncertainty to provide a pixel-wise uncertainty estimation regarding light color prediction, which helps avoid learning from incorrect labels. Our model demonstrates a significant improvement in accuracy, achieving 21.5% lower Mean Angular Error (MAE) compared to the state-of-the-art method on our nighttime dataset.