:fire: PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. :fire: 图像翻译，条件GAN，AI绘画

Grayscale Image Colorization with Generative Adversarial Networks. https://arxiv.org/abs/1803.05400

GANs to color grayscaled manga , now with dataset! :)

This project is used for manga colorization with reference image guided based on GAN

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more. The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.

Colorization of greyscaled images with GAN

Hair Style and Color changing with GANs, deployed on interactive Graphical User Interface (GUI) with PyQt.

Landscape and face image colorization with Generative Adversarial Networks (GANs)

Grayscale image colorization with masks and GANs

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Powerful, fast and convenient. Colorizes videos with GAN technology. Powdered by Xenon

Image colorization with GAN

Automatic manga colorization using convolutional neural networks (Pix2Pix GAN with U-Net and PatchGAN) in LAB color space

GAN colorization project with the movie The Lighthouse. Special thanks to ArVis Technology for allowing the usage of their GPU for the project.

The problem addressed in this project revolves around generating Anime Faces using a GAN model with the additional capability of selecting hair color.

Colorize 🌈 black and white images with 📸 Pix2Pix GANs.

My capstone project proposal for my ML Engineer Nanodegree - Image Colorization with GANs

Trained an image colorization model from scratch using conditional GAN (UNet with ResNet18 backbone) and also implemented and analyzed two pipelines involving blur image colorization/super resolution , where for implementing super resolution ESRGAN and Real ESRGAN were used.

A 3-part pipeline to make synthetic game images more realistic using ISP, Super-Resolution, and GAN discriminators. Improves color, detail, and realism with FID/KID evaluation against Cityscapes.

This is a reimplementation of Colorizing-with-GANs in PyTorch, see this link for more information (https://github.com/ImagingLab/Colorizing-with-GANs)

Creating two models for colorization of Black and White Images into RGB format, and comparing the two models, highlighting the importance of what features we select while creating a model.

Image Colorization with GAN, Ara Guler.

Image colorization task with GAN based approach

Fix Images resolution or color with basic GAN and CNN algorithms

Deep GANs color correction method uses to reconstruct a clean color image by deep convolutional neural networks (CNNs) traing on gray images with ground truth.

Colorization and Enhancement of Old Images Using Generative Adversarial Networks(GAN) with Pix2pix algorithm.

Semantic-aware image colorization with ResNet-UNet GAN, LPIPS perceptual loss, and optional FAISS retrieval hints. Colab-ready. TOPICS: image-colorization, gan, unet, resnet, pytorch, lpips, faiss, computer-vision, colab, deep-learning

🖼️🎨 This repository presents a deep learning approach to colorizing grayscale images using Generative Adversarial Networks (GANs). Built with TensorFlow and Keras, the model converts grey-scale images into colored images.

This project aims to use a color compensation algorithm and a GAN to recolor problematic colors for those with color vision deficiency (CVD)

This project utilizes GANs for converting grayscale images to color, leveraging deep learning models to enhance colorization accuracy. It features customizable training pipelines, evaluation metrics, and supports further improvements with new models and techniques.