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Cited by 1,376

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Learning to See in the Dark

Chen, Chen, Chen, Qifeng, Xu, Jia, Koltun, Vladlen·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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 induce blur 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.

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Research works printed but not linked: 3

  1. remez, 2017, Deep convolutional denoising of low-light images
  2. 10.1109/TIP.2017.2713942 · doi:10.1109/tip.2017.2713942
  3. zhang, 2012, Enhancement and noise reduction of very low light level images, ICPR

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Metadata

Authors
Chen, C., Chen, Q., Xu, J., & Koltun, V.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
3291-3300
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2799265886
Cited by
1,376
References
42
FWCI
32.69
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning to See in the Dark. https://app.cochat.ai/papers/10.1109/cvpr.2018.00347
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Chen, C., Chen, Q., Xu, J., & Koltun, V. (2018). Learning to See in the Dark. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00347

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