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Researchers Devise AI System To Reduce Noise in Photos (venturebeat.com)
Researchers from Nvidia, MIT, and Aalto University are using artificial intelligence to reduce noise in photos. The team used 50,000 images from the ImageNet dataset to train its AI system for reconstructing photos, and the system is able to remove noise from an image even though it has never seen the image without noise. VentureBeat: Named Noise2Noise, the AI system was created using deep learning and draws its intelligence from 50,000 images from the ImageNet database. Each came as a clean, high-quality image without noise but was manipulated to add randomized noise. Computer-generated images and MRI scans were also used to train Noise2Noise. Denoising or noise reduction methods have been around for a long time now, but methods that utilize deep learning are a more recent phenomenon.
Did you even check the paper at: https://arxiv.org/pdf/1803.041...
The abstract states "We apply basic statistical reasoning to signal reconstruction by machine learning — learning to map corrupted observations to clean signals — with a simple and powerful conclusion: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones , at performance close or equal to training using clean exemplars."
The results show dramatic improvements that are very close to original image (before random noise is introduced to generate the input)- a level of improvement that is simply not possible with conventional image processing/denoising filters.
If this is not AI, I don't know what else would be.
The paper includes similar experiments with Poisson noise instead of gaussian noise. The neural network does need to be trained differently depending on the type of noise.
It will never be equivalent image quality, since the 50% exposure contains less information. Possibly it will be good enough, but the 50% exposure will possibly be good enough anyway without the neural network. The neural network is literally making up information based on what it remembers from the training data, which seems like an incredibly bad idea to apply to MRI scans.