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Frame from CVPR 2018: Learning to See in the Dark Plays via YouTube's privacy-enhanced embed

CVPR 2018: Learning to See in the Dark

Chen Chen
Demos
YouTube
1 HN user recommended this demo1 month2018–2020 score 1
Years with an HN recommendation, 2007–2026

HN commenters flag it as a remarkable machine-learning result, notably for showing that training on raw images beats JPEG.

A short video accompanying the CVPR 2018 paper Learning to See in the Dark. It shows a neural network producing sharp images from very low-light raw sensor data, compared against denoising approaches.

“Remarkable machine learning result for "producing astoundingly sharp photos in very low light” (HN, 2018)

1 recommendations · 2 citations · Demos · intermediate · partly dated

3 Hacker News comments

From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.

@isp May 2018 Recommends
on HN thread Learning to See in the Dark
Remarkable machine learning result for "producing astoundingly sharp photos in very low light" (Cory Doctorow - [link] ) Demo example (one of many examples - drag the middle slider from left-to-right): [link] GitHub: [link] Paper: [link] Video: [this video]
Remarkable low-light image enhancement result.

Citations, criticism and jokes

@lovelearning Aug 2018 Cites it
on HN thread Why OpenCV uses BGR color space (2015)
Read more
Public raw image datasets are rare, probably because most cameras, except DSLRs, don't support storing raw images. Also, the nature of the inference may not require high fidelity raw images since they're going to be down-sampled anyway. Unless research suggests that raw images improve inference metrics, nobody's going to invest effort into building a raw image dataset. One notable recent exception to this is the "Learning to see in the dark" paper on low light image enhancement[1] - they found that training on raw images gives much better enhancement than JPEG. The data availability problem may start getting solved in the near future because mobiles (atleast Android) support storing and processing raw images, and on-device learning and inference capabilities are getting better all the time on mobiles. [1]: [this video]
Cited as example of training on raw images improving enhancement.
@pera Apr 2020 Cites it
on HN thread Learning to See in the Dark (2018)
To me the results seem vastly superior to those sort of simple DSP algorithms. The video shows a comparison with some denoising: [this video]
Video shows comparison against denoising approaches.

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