Google’s RAISR sharpens low-resolution images using machine learning

Google raisr image upscaling super resolution 9 png
Via Newatlas

From a jagged low-res jpeg to a sharper, larger image file, Google researchers have found a way to use machine learning to upscale images to higher resolutions at lightning speed, and it works so fast it could one day be built into your smartphone.

Google is using machine learning to upscale jpeg images much, much faster and often more accurately than current processor-intensive upsampling methods.

Its RAISR program (Rapid and Accurate Image Super Resolution) is still at the experimental stage, but it’s already operating between 10 to 100 times as fast as existing upscaling technology and getting better results in many cases.

The system learns by taking in thousands of pairs of images – one at full resolution, the other downsampled to a jagged, low-res image. It pores over these pairs to work out which filters it can apply to the low-res image’s pixels to get them closest to what’s in the full-res file, taking context into account.

Within about an hour, it’s gone through some 10,000 image pairs and built a pretty decent little knowledge base that it can then apply to any low-res image.

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