Learning to See in the Dark

Chen Chen, Qifeng Chen, Jia Xu and Vladlen Koltun

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Figure. Extreme low-light imaging by a Sony a7S II camera using ISO 8000, f/5.6, 1/30 second. Dark indoor environment. The illuminance at the camera is <0.1 lux.



Two minute papers by Karoly Zsolnai-Feher


Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can lead to blurry images and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure night-time images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work.

Code and dataset

Please check the [Github page] for instructions.



High resolution results (Click for details. Drag the slider bar.)

Compare with the traditional pipeline

Compare with the traditional pipeline followed by BM3D

Compare with the HDRNET [Gharbi et al. SIGGRAPH 2017]

Compare with commercial software Adobe Camera Raw and Sony Capture One on image colors (brightness adjusted in the software for visualization)

Controlled experiments (See paper for details)


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