Open Source Image Enhancement Projects

We are researching open source image enhancement technologies. Here’s a starter list, which comes from this article: https://ai.plainenglish.io/top-5-open-source-image-super-resolution-projects-to-boost-your-image-processing-tasks-e6008c978685

Do you have any experience with these projects? Or ones not listed here? Please let us know.

1. Waifu2x — 19, 833 stars

Github | Official Documentation

Waifu2x is an open-source and free Image Super-Resolution for Anime-style art using Deep Convolution Neural Networks for images.

It supports two features for the given input image:

  • 2X upscaling
  • Noise reduction

It is ready to use and the demo application can be found at http://waifu2x.udp.jp/ .

2. Anime4K — 12, 402 stars

Github | Official Documentation

Anime4K is a set of open-source, high-quality real-time anime upscaling/denoising algorithms that you can implement in any programming language.

The simplicity and speed of Anime4K allow the user to watch upscaled anime in real-time.

3. Singan — 2, 662 stars

Github | Official Documentation

Singan is the Official PyTorch implementation of the paper: “SinGAN: Learning a Generative Model from a Single Natural Image,” a new unconditional generative model trained on a single natural image.

Singan is trained to capture the internal distribution of patches within the image and is then able to generate high-quality, diverse samples that carry the same visual content as the image.

Here is the link to their Youtube Video.

Link to paper.

4. SRGAN (Super Resolution using Generative Adversarial Network)— 2, 600 stars

Github

Singan is the official Tensorflow Implementation of the paper “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.”

5. Image-Super-Resolution — 2, 362 stars

Github | Official Documentation

Image Super-Resolution is an open-source project to upscale and improves the quality of low-resolution images.

This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) and scripts to train these networks using content and adversarial loss components.

The implemented networks include: