Pixel-art scaling algorithms are graphical filters that are often used in video game emulators to enhance hand-drawn 2D pixel art graphics. The re-scaling of pixel art is a specialist sub-field of image rescaling.
As pixel-art graphics are usually in very low resolutions, they rely on careful placing of individual pixels, often with a limited palette of colors. This results in graphics that rely on a high amount of stylized visual cues to define complex shapes with very little resolution, down to individual pixels. This makes image scaling of pixel art a particularly difficult problem.
A number of specialized algorithms[1] have been developed to handle pixel-art graphics, as the traditional scaling algorithms do not take such perceptual cues into account.
Since a typical application of this technology is improving the appearance of fourth-generation and earlier video games on arcade and console emulators, many are designed to run in real time for sufficiently small input images at 60 frames per second. This places constraints on the type of programming techniques that can be used for this sort of real-time processing. Many work only on specific scale factors: 2× is the most common, with 3×, 4×, 5× and 6× also present.
Plugin for GIMP : https://github.com/bbbbbr/gimp-plugin-pixel-art-scalers
Waifu2x
https://en.wikipedia.org/wiki/Waifu2x
https://github.com/lltcggie/waifu2x-caffe/releases
https://github.com/imPRAGMA/W2XKit
https://old.reddit.com/r/WaifuUpscales/new/
https://github.com/BlueCocoa/waifu2x-ncnn-vulkan-macos/releases
https://old.reddit.com/r/Dandere2x/
https://old.reddit.com/r/waifu2x
https://github.com/AaronFeng753/Waifu2x-Extension
https://github.com/K4YT3X/video2x
https://old.reddit.com/r/AnimeResearch
Quote from a reddit comment :
A short list, ordered after output quality and setup time:
SRGAN, Super-resolution generative adversarial network : https://github.com/topics/srgan,
Other implementations: https://github.com/tensorlayer/srgan
https://github.com/brade31919/SRGAN-tensorflow
https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
Neural Enhance: https://github.com/alexjc/neural-enhance/
Photoshop: The newest PS version (19.x, since October 2017 release) also has a new upscaling method, called "Preserve Details 2.0 Upscale" but compared to SRGAN the results clearly lack sharp and fine details. You have asked for an App and PS is easy to use and can be automated.
Overview of the most popular algorithms:
https://github.com/IvoryCandy/super-resolution
(VDSR, EDSR, DCRN, SubPixelCNN, SRCNN, FSRCNN, SRGAN)
Not in the list above:
LapSRN: https://github.com/phoenix104104/LapSRN
SelfExSR: https://github.com/jbhuang0604/SelfExSR
RAISR, developed by Google:
https://github.com/MKFMIKU/RAISR
https://github.com/movehand/raisr