See in the Dark: a machine learning technique for producing astoundingly sharp photos in very low light

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]

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