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.
Waifu2x is an open-source and free Image Super-Resolution for Anime-style art using Deep Convolution Neural Networks for images.
- 2X upscaling
- Noise reduction
It is ready to use and the demo application can be found at http://waifu2x.udp.jp/ .
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.
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.
Link to paper.
Singan is the official Tensorflow Implementation of the paper “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.”
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 super-scaling Residual Dense Network described in Residual Dense Network for Image Super-Resolution (Zhang et al. 2018)
- The super-scaling Residual in Residual Dense Network described in ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (Wang et al. 2018)
- A multi-output version of the Keras VGG19 network for deep features extraction is used in the perceptual loss.
- A custom discriminator network based on the one described in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGANS, Ledig et al. 2017)