Posted on Tuesday, July 10 2018 @ 13:54 CEST by Thomas De MaesschalckRecent deep learning work in the field has focused on training a neural network to restore images by showing example pairs of noisy and clean images. The AI then learns how to make up the difference. This method differs because it only requires two input images with the noise or grain. Without ever being shown what a noise-free image looks like, this AI can remove artifacts, noise, grain, and automatically enhance your photos.
“It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars,” the researchers stated in their paper.“[The neural network] is on par with state-of-the-art methods that make use of clean examples — using precisely the same training methodology, and often without appreciable drawbacks in training time or performance.”
Debuting at #ICML2018: See how @NVIDIA researchers are using #deeplearning and #GPUs to help restore grainy or noisy images by simply looking at examples of corrupted photos only. https://t.co/i90YJK4SO8 pic.twitter.com/axNp5zwvvz
— NVIDIA AI Developer (@NVIDIAAIDev) July 10, 2018
