(edit1 : this got to the top of r/machinelearning, check out the comments for some discussion)
Recently a very impressive paper came out which produced some extremely life-like images generated from a neural network. Since I wrote a post about convolutional autoencoders about 9 months ago, I have been thinking about the problem of how one could upscale or ‘enhance’ an image, CSI-style, by using a neural network to fill in the missing pixels. I was therefore very interested while reading this paper, as one of its predecessors was attempting to do just that (albeit in a much more extreme way, generating images from essentially 4 pixels).
Using this as inspiration, I built a neural network with the DCGAN structure in Theano, and trained it on a large set of images of celebrities. Here is an example of a random outputs, the original images are on the left, the grainy images fed into the neural network in the middle, and the outputs on the right.
N.B. this image is large, you should open and zoom in to really see the detail / lack of detail produced by the DCGAN, I certainly do not claim the DCGAN did phenomenally well on pixel level detail (although occasionally I’d say it did a pretty impressive job, particularly with things like hair)