Using Noise To Void https://arxiv.org/abs/1811.10980 for fiji, which can be trained to recognize noise from just many unlabeled images. It is best for noise that it is due to poor signal, like for instance is the case if you have an old weak x-ray source. (6-7 microamps at 60kv, now).  

This is a kit kat bar, and that is actually a pretty substantial difference. Whenever you do this, you're in a sense corrupting the image, but generally to a state that is more pleasing to the eye. 

This is what the kit kat projections look like with and without edge finding (the "filtered" part of filtered back projection). 

It's not meant for structured noise, but it still doesn't do a terrible job at it anyway. AI is weird. 

I'll close this tiny update with a little video of Mr. Lego man. This kind of scan, that shows such obvious signal to noise problems (being scanned in 5 minutes, which is very fast for this machine), would benefit the most from ML denoising.