A group of scientists from Intel and the University of Illinois at Urbana–Champaign have published a paper called Learning to See in the Dark detailing a powerful machine-learning based image processing technique that allows regular cameras to take super-sharp pictures in very low light, without long exposures or the kinds of graininess associated with low-light photography.
The results are astounding.
We propose a new image processing pipeline that addresses the challenges of extreme low-light photography via
a data-driven approach. Specifically, we train deep neural
networks to learn the image processing pipeline for low-
light raw data, including color transformations, demosaic-
ing, noise reduction, and image enhancement. The pipeline
is trained end-to-end to avoid the noise amplification and
error accumulation that characterize traditional camera pro-
cessing pipelines in this regime.
Learning to See in the Dark [Chen Chen, Qifeng Chen, Jia Xu and Vladlen Koltun/Arxiv]
Learning to See in the Dark [Chen Chen, Qifeng Chen, Jia Xu and Vladlen Koltun/University of Illinois]
(via Four Short Links)
I'm in the midst of couple of weeks' worth of lectures, public events and teaching, and you can catch me in Toronto (for Word on the Street, Seeding Utopias and Resisting Dystopias and 6 Degrees); Newry, ME (Maine Library Association) and Portland, ME (in conversation with James Patrick Kelly).
Octavia Butler (previously), the brilliant Afrofuturist, McArthur Genius Grant-winning science fiction writer, died far, far too soon, leaving behind a corpus of incredible, voraciously readable novels, and a community of writers who were inspired by her example.
EFF has just posted a job listing for a development director, seeking someone to "take charge of EFF's eleven-person Development Team in their efforts to raise over $13 million each year," starting late 2019 or early 2020.
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