Released in 1998, the Game Boy camera was perhaps the first digital camera many young hackers got their hands on. Around the time Sony Mavica cameras were shoving VGA resolution pictures onto floppy drives, the Game Boy camera was snapping 256×224 resolution pictures and displaying them on a 190×144 resolution display. The picture quality was terrible, but [Roland Meertens] recently had an idea. Why not use neural networks to turn these Game Boy Camera pictures into photorealistic images?
Neural networks, deep learning, machine learning, or whatever other buzzwords we’re using require training data. In this case, the training data would be a picture from a Game Boy Camera and a full-color, high-resolution image of the same scene. This dataset obviously does not exist so [Roland] took a few close up head shots of celebrities and reduced the color to four shades of gray.
For the deep machine artificial neural learning part of this experiment, [Roland] turned to a few papers on converting photographs to sketches and back again, real-time style transfer. After some work, this neural network turned the test data back into images reasonably similar to the original images. This is what you would expect from a trained neural network, but [Roland] also sent a few pics from the Game Boy Camera through this deep machine artificial learning minsky. These images turned out surprisingly well – a bit washed out, but nearly lomographic in character.
We’ve seen a lot of hacks with the Game Boy Camera over the years. Everything from dumping the raw images with a microcontroller to turning the sensor into a camcorder has been done. Although [Roland]’s technique will only work on faces, it is an excellent example of what neural networks can do.