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Deep Learning Can Now Flawlessly Correct Photos Taken in Almost Complete Darkness

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James Williams
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There are typically two approaches to taking usable photos in low-light conditions.

You can either use a slow shutter, which requires a tripod to eliminate blur, or electronically increase the sensitivity of a camera’s sensor, which introduces ugly noise artefacts.

But there’s now a third approach that takes advantage of machine learning to artificially boost the brightness of a dark photo afterwards—with stunning results.

Researchers at Intel and the University of Illinois Urbana–Champaign have come up with what might be the ultimate post-production tool for photographers who often find themselves shooting in low-light scenarios like performances at concert venues, or capturing nocturnal wildlife at night.

But it can even be used to improve the quality of the smartphone photos you snapped at a dark and seedy bar.

As with countless other image processing innovations as of late, the research, which was recently published in a paper titled “Learning to See in the Dark,” takes advantage of deep learning techniques to train an algorithm on how a poorly exposed image should be properly brightened and colour-corrected during post-processing.

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