Our customers often ask us for specific functions and filters to be included in our software. Paying attention to user requests and managing our development priorities according to them is probably one of the things Amped Software is best known for.
However, not all requests, even if technically feasible, are a match for the purpose of our solutions. Take for example Amped FIVE: some of the most common use cases are enhancing license plates or faces.
Quite a few customers asked for some functions to perform super resolution of faces from a single image. While this may be technically very interesting, most of the implementations have a fatal flaw that prevents them from being used for forensic applications: they introduce data external to the case.
Yesterday a GitHub project called “srez” caught my attention.
Image super-resolution through deep learning. This project uses deep learning to upscale 16×16 images by a 4x factor. The resulting 64×64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Here’s an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16×16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
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