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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.
I have a couple of thousand images that would benefit from noise reduction. Shooting movement in low light means high ISO or blur, so I accept the noise.
If they wanted some serious training data, the whole astrophotography field is full of people that take dozens of pictures of the same thing then sample across all of them to remove noise. That means they have plenty of randomness in their noisy images and a nice clean one for comparison.
One thing I've been slowly trying to get going as a side project is exploring the use of neural networks to process raw image data.
A lot goes into processing a raw image, there is conversion of data from various color matrices, generally some sharpening, and also noise reduction. It seems like a good neural net could possibly handle all aspects and maybe do a better job if trained well, as it might spot patterns in noise or color conversion that algorithm designers to date have not (well except for recognizing color swatches and altering processing based on that... )
I was thinking to train you could just do some very accurate high res close up images of a variety of subjects that were very carefully color corrected. Then you would take images from a wider FOV or farther away, so that you could use the high-res images to determine what a "real" output pixel should be, vs whatever the result of combining various sensor data would be to produce a result.
Seems like a lot of potential here beyond just noise reduction...
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I'm curious how the results stack up against commercial options like in Lightroom or Aperture. If these can reduce noise without softening the image, I'd be very interested in getting it.
I wish they'd quit with the AI and Artificial Intelligence monikers being applied to everything in tech these days. The day one of these AI's tells me that, no, it won't brew my coffee this morning because it is taking the day off is the day I might buy in to this nonsense.
It would be interesting to see a visual diff between the denoised result and the source image before the random noise was added, in order to see what kinds of artifacts were generated during the denoising process. For example, did it add any leaves to the image of the koala?
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While the images they have shown as examples are really pretty impressive, given that they're using a training set of Image A versus Image A Plus Noise, the problem is akin to blind source separation (BSS). There's been quite a lot of work done on BSS, much of which is very impressive (and based on neural nets).
The critical issue is to see what happens when they take a real photograph that has not been adulterated to add noise, and improve that. Will their model of a noiseless source image with additive noise still hold? The article doesn't touch upon that critical test, unfortunately.
The results they show are very, very cool, though. And if they hold up for MRI work, it would be a game-changer in the medical field. The article shows an MRI adulterated with noise, their recovered image, and the noiseless ground truth. A better test would be to take an MRI that was scanned for too short a time (and thus is noisy), and compare their extraction against an MRI with identical scanning parameters, except for normal imaging time. MRI magnet time is expensive; if it can be reduced by 50% and get equivalent image quality, that's a huge advance.
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