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:
Super-resolution imaging is a class of techniques that enhance the resolution of an imaging system (Wikipedia). The entertainment series CSI has been ridiculed for relying on exaggerated and unrealistic applications of it:
As a result, there are now several applications where machines have learned to literallyfill in the blanks in imagery. Most notable seems the method developed by Google: Rapid and Accurate Image Super Resolution, or RAISR is short. In contrast to other approaches, RAISR does not rely on (adversarial) neural network(s) and is thus not as resource-demanding to train. Moreover, it’s performance is quite remarkable:
I guess you’re eager to test this super resolution out yourself?! letsenhance.io let’s you enhance the resolution of five images for free, after which it charges you $5 per twenty pictures processed. The website feeds the input image to a neural net and puts out an image of which the resolution has been increased four fold! I tested it with this random blurry picture I retrieved from Google/Pinterest.
Do you see how much more detailed (though still blurry) the second image is? Nevertheless, upscaling four times seems about the limit as that is the default factor for both RAISR and Let’s Enhance. I am very curious to see how this super resolution is going to develop in the future, how it will be used to decrease memory or network demands, whether it will be integrated with video platforms like YouTube or Netflix, and which algorithm will ultimately take the crown!