N2D-GAN: A Night-to-Day Image-to-Image Translator
Xiaopeng Li, Xiaojie Guo, Jiawan Zhang · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Existing image-to-image translation methods can effectively deal with simple scenes or styles, such as horses-to-zebra, cat-to-dog, and summer-to-winter. However, the performance of night-to-day (N2D) translation remains unsatisfied due to imbalanced/poor visibility and thus translation ambiguity, although some progress has been made by GAN-based methods recently. This paper proposes a CycleGAN-based N2D translation scheme, namely N2D-GAN, in a weakly-supervised manner with consideration of both image and semantic information. Specifically, we first adjust the brightness of night-time images to boost the visibility, so that the generator can better extract content information. Then, the generator processes the translation by enforcing results to follow the distribution of daytime images in both image and semantic domains. Besides, the cycle consistency is introduced to preserve the fidelity between translations from two directions. Experimental results demonstrate that our strategy outperforms other state-of-the-art N2D methods both quantitatively and qualitatively.