In the video below, one of my favorite YouTube channels (Two Minute Papers) discusses a new super resolution project where academic scholars taught a neural network to improve low quality photo’s. The researchers took the same picture with multiple camera’s of varying quality and allowed a neural network to learn how the lowest quality pictures can be adjusted to more closely resemble their high quality counterparts. A very interesting approach and the results are just mind-boggling:
The scholars were nice enough to not only publish the paper open access, but also to open source the data. You can download a 125 Mb sample here or the original full 64 GB dataset here.
Aaron Jackson, Adrian Bulat, Vasileios Argyriou and Georgios Tzimiropoulos
of the Computer Vision Laboratory of the University of Nottingham built a neural network that generates a full 3D image of a single portrait photograph. They turn a photograph like this…
… into an accurately creepy 3D image like this.
You can try it with your own or other photographs here. I found that images with white background get the best results. On their project website you can read more about the underlying convolutional neural network.
Update 21-10-2017: One of my favorite YouTube channels explains how the models were trained and the data used: