SID
High-quality Image
Image Denoising
|...
许可协议: MIT

Overview

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.

Citation

@InProceedings{Chen_2018_CVPR,
author = {Chen, Chen and Chen, Qifeng and Xu, Jia and Koltun, Vladlen},
title = {Learning to See in the Dark},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

License

MIT

数据概要
数据格式
Image,
数据量
--
文件大小
76.68GB
发布方
Chen Chen
Research Scientist at Apple AI and Machine Learning
数据集反馈
出错了
刚刚
timeout_error
立即开始构建AI
出错了
刚刚
timeout_error