Image signal processing (ISP) is crucial for camera imaging, and neural
networks (NN) solutions are extensively deployed for daytime scenes. The lack
of sufficient nighttime image dataset and insights on nighttime illumination
characteristics poses a great challenge for high-quality rendering using
existing NN ISPs. To tackle it, we first built a high-resolution nighttime
RAW-RGB (NR2R) dataset with white balance and tone mapping annotated by expert
professionals. Meanwhile, to best capture the characteristics of nighttime
illumination light sources, we develop the CBUnet, a two-stage NN ISP to
cascade the compensation of color and brightness attributes. Experiments show
that our method has better visual quality compared to traditional ISP pipeline,
and is ranked at the second place in the NTIRE 2022 Night Photography Rendering
Challenge for two tracks by respective People's and Professional Photographer's
choices. The code and relevant materials are avaiable on our website:
this https URL

该研究建立了高分辨率夜间 RAW-RGB 数据集，并开发了 CBUnet，一个二阶段的神经网络图像信号处理方案，以应对夜间光照特性不明确的问题，实验结果表明，该方法相较于传统 ISP 管道具有更好的视觉效果，并在 NTIRE 2022 夜间摄影渲染挑战赛上排名第二。