Qtwaifu2x
Version: GIT Web: https://github.com/ cmdrkotori/qtwaifu2x
Some things in life need extra care and attention, if future generations are to appreciate them. Examples could include an old, crumbling book or a classic cartoon whose visuals have degraded over time. In the wilds of Github you can find several ways to stretch raster images, which obviously you can’t add missing details to, but you can still enhance. Then there’s the xBR scaling method based on pattern recognition, the user-friendly Smilla
Enlarger that makes use of a home-grown fractal-based interpolation mechanism, and a bunch of ImageMagick filters that you can use with the convert command. And then there’s Waifu2x, a technology that beats all the aforementioned competitors and does things with images that border on magic.
Waifu2x improves image resolution using deep convolutional neural networks–a machine-learning technique. Waifu2x delivers superb results with pixel art, sketches and anime art, but it also support photographs. One of the requirements of the original
Waifu2x is the CUDA toolkit, which only works with Nvidia GPUs. Yes, upscaling is a resource-heavy job, and
Waifu2x needs a powerful GPU to offload part of the work onto it. But, for everyone with Intel or Radeon chips, there’s the alternative Waifu2x-converter-cpp version that works like a ‘software renderer’ and feels fine with any hardware setup. It relies on Picojson and OpenCV 3 and is quite easy to build it from source.
Qtwaifu2x is a front-end to Waifu2x-converter-cpp and is a nice way to play with high resolutions without using the command line. Upscaling images using a CPU-only method is slower and we recommend using small files for test runs before working with full-sized images. Still,
Qtwaifu2x and the forked version of Waifu2x it relies upon is perhaps the most affordable way to enjoy high resolutions with a modest hardware setup.