Enhancing images using Deep Convolutional Generative Adversarial Networks (DCGANs)

(edit1 : this got to the top of r/machinelearning, check out the comments for some discussion)

(edit2 : code for this project can now be found at this repo, discussion has been written up here)

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.

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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)

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