Neural Networks in FixPix
The following are all the neural network components currently supported by FixPix (as plugins
that are downloadable from within FixPix). More plugins with exciting new features continuously being added.
U2-Net
U2-Net is a neural network that can detect and separate the main object in a picture from its background.
This is done using a special design that allows the program to analyze the picture in great detail without
using too much computer memory.
One of the things you can do with U2-Net is to create sketches from photos. You can also use it to change
the background of a picture, for example, by removing the original background and replacing it with
something else. There are many other creative ways to use U2-Net as well.
The main contributors to U2-Net are Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R.
Zaiane and Martin Jagersand. They developed the model and published their paper "U2-Net: Going
Deeper with Nested U-Structure for Salient Object Detection" in Pattern Recognition in 2020.
AnimeGAN2
AnimeGANv2 is an improved version of AnimeGAN, a GAN for transforming a photo to have Anime style. It was developed by Asher Chan. It uses a type of artificial intelligence called a GAN (Generative Adversarial etwork) to enable the program to analyze the photo and create a new image that looks like it was drawn
in an anime style.
GFPGAN
GFPGAN is a neural network that uses advanced techniques to restore old or low-quality images of faces. It
does this by using information from a large collection of high-quality face images to fill in missing
details and improve the overall appearance of the restored image.
The main contributors to the project are Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan.
Colorful Image Colorization
Image colorization is the process of adding color to black and white or grayscale images.
In 2016, Richard Zhang, Phillip Isola, and Alexei A. Efros published a paper titled "Colorful Image Colorization" in which they presented a CNN for colorizing gray images. They trained the network with 1.3
million images.
DeOldify
DeOldify by Jason Antic is an image colorization method which has improved upon the original "Colorful
Image Colorization" method and provides two separate machine learning models for colorizing a grayscale
image.
AdaAttN
AdaAttN is a tool that enables adding artistic styles to images. It does this by paying attention to the details in both the original image and the style image, and then combining them in a smart way. This results in high-quality images that have the artistic style of one image applied to the content of another. It's like having a computer artist that can paint your photos in the style of famous artists.
The FixPix AdaAttN plugin enanbles you to select any image as a style to be used and change any image so that it looks like that style.
AdaAttN stands for Adaptive Attention Normalization and was proposed by Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, and Errui Ding in their paper "AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer"
Fast Neural Style
Fast Neural Style is a technique that allows computers to quickly add artistic styles to images. The method is much faster than the original neural style transfer algorithm, which can take several minutes to produce a single stylized image. This model does a good job of transferring the "feel" and style to the target image, but unlike AdaAttN, a different model has to be trained for every new style. The FixPix plugin for Fast Neural Style comes prebundled with 10 different styles.
It was developed by Justin Johnson, Alexandre Alahi, and Li Fei-Fei and presented at ECCV 2016.
CartoonGAN
CartoonGAN is a deep learning model for transforming photos of real-world scenes into cartoon style images. It was developed by Yang Chen, Yu-Kun Lai, and Yong-Jin Liu and presented at CVPR 2018