[ICCV 2023] DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
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Updated
Oct 25, 2024 - Jupyter Notebook
[ICCV 2023] DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
PyTorch reimplementation of Interactive Deep Colorization
📚 A collection of Deep Learning based Image Colorization and Video Colorization papers.
In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
🎨 Automatic Image Colorization using TensorFlow based on Residual Encoder Network
Deep Learning Applications (Darknet - YOLOv3, YOLOv4 | DeOldify - Image Colorization, Video Colorization | Face-Recognition) with Google Colaboratory - on the free Tesla K80/Tesla T4/Tesla P100 GPU - using Keras, Tensorflow and PyTorch.
[SIGGRAPH Asia 2019] Language-based Colorization of Scene Sketches
Image colorization using CNNs in tensorflow
A from-scratch PyTorch implementation of "Colorful Image Colorization" by Zhang et al.
Image and video colorizer is package for automatic image and video colorization. Models are allready trained
Official Pytorch implementation of Adversarial Segmentation Loss for Sketch Colorization [ICIP 2021]
Demonstration implementations of neural network image processing algorithms
A Collection of Low Level Vision Research Groups
🖌️ Automatic Image Colorization with Simultaneous Classification – based on "Let there be Color!"
This repository is for "Image Colorization: A Survey and Dataset" paper
A unified ML platform for Image Transformation (style transfer, image colorization, sketching and pixelating)
Transform grayscale photos to color photos in iOS
This repository provides an interactive image colorization tool that leverages Stable Diffusion (SDXL) and BLIP for user-controlled color generation. With a retrained model using the ControlNet approach, users can upload images and specify colors for different objects, enhancing the colorization process through a user-friendly Gradio interface.
Speech bubble aware automatic comic colorization using CNN
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