Distinguishing images from noise
Neural networks have been successfully used in supervised learning to classify images.
I'm currently looking at a different problem: distinguishing images that show something, contain some manifest order, versus images that are just white noise. I'd like to get some numeric estimate of just how ordered an image is.
Clearly there are several ways of doing this, e.g. PNG compressing the image and looking at how much it shrank. (Pure noise is not compressible.) But I am currently interested in trying to do it with neural networks; it seems to me that it should be possible to do this with unsupervised learning, using some sort of autoencoder.
Is this something that has been done? Ideally with PyTorch but I would be happy to look at a solution using any language and framework.

This question is a bit too broad for stackoverflow, it seems more like an research question. The topic would also be more at home in https://stats.stackexchange.com/ , where machine learning people lurk.
Interesting question though. In my experience going with an analytic solution is always better than trying to learn a solution if possible, I see machine learning as something to fall back on when things get too complex. Detecting images of pure noise is relatively simple, one would expect the Shannon entropy of the image to explode. You could also e.g. match the K-L divergence of the image histogram to a Gaussian or Poisson distribution or whatever the noise distribution you'd expect. If the images are really just noise then I'd expect any of these solutions to classify them very easily and fast with very little tweaking. If you want to learn the answer I'd recommend crafting few information theoretic or simple edge based etc. features and classifying them e.g. with Naive Bayes or an SVM.
If you want to use neural networks then sure, I'm sure you could teach a model to get the concept of information content. Deep image prior is a popular recent article which uses the idea of an autoencoder having lower 'impedance' for structured images. They use it to denoise, impute and do superresolution but I'm sure you could use make an unsupervised or semi-supervised NN based nonsense-detector by following the same train of thought. I'd expect it to be orders of magnitude slower than an simpler hand-crafted information theoretic approach with similiar accuracy or worse.
