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创建来自Hello Dataset / Robert
概要
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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

数据集信息
应用场景Image Denoising
标注类型High-quality Image
LicenseMIT
更新时间2021-03-24 22:51:39
数据概要
数据格式Image
数据数量0
文件大小77MB
标注数量0
版权归属方
Chen Chen
标注方
未知
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