Learning to See in the Dark
Abstract
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 low-light 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.
Cite
Text
Chen et al. "Learning to See in the Dark." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00347Markdown
[Chen et al. "Learning to See in the Dark." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/chen2018cvpr-learning/) doi:10.1109/CVPR.2018.00347BibTeX
@inproceedings{chen2018cvpr-learning,
title = {{Learning to See in the Dark}},
author = {Chen, Chen and Chen, Qifeng and Xu, Jia and Koltun, Vladlen},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00347},
url = {https://mlanthology.org/cvpr/2018/chen2018cvpr-learning/}
}