SPN2D-GAN: Semantic Prior Based Night-to-Day Image-to-Image Translation
Xiaopeng Li, Xiaojie Guo · IEEE Transactions on Multimedia · 2022
Existing image-to-image translation approaches can deal with simple scenes or styles effectively, such as summer-to-winter, horses-to-zebra, and photo-to-map. Although a great progress has been made by GAN-based methods recently, the performance of night-to-day (N2D) translation remains unsatisfactory due to imbalanced/poor visibility, and thus leading to translation ambiguity. To improve the quality of N2D translation, we propose an unpaired translation scheme based on a semantic prior generator, namely SPN2D-GAN, in a weakly- supervised manner with consideration of both image and semantic information. Specifically, we design a novel N2D generator, which can adopt the semantic information of images as prior knowledge to generate more reasonable and realistic results. Also, we suggest adjusting the brightness of nighttime images to boost the visibility, so that the generator can better extract content information. Moreover, the proposed SPN2D-GAN translates images by enforcing the distribution of daytime images in both image and semantic domains on final outputs. Besides, the cycle consistency is employed to preserve the fidelity between translations from two directions. Extensive experimental results are provided to reveal the effectiveness of our design, and demonstrate its superior performance over other state-of-the-art N2D translation approaches both quantitatively and qualitatively.