153 papers with code • 2 benchmarks • 7 datasets

Colorization is the process of adding plausible color information to monochrome photographs or videos. Colorization is a highly undetermined problem, requiring mapping a real-valued luminance image to a three-dimensional color-valued one, that has not a unique solution.

Source: ChromaGAN: An Adversarial Approach for Picture Colorization


Use these libraries to find Colorization models and implementations

Most implemented papers

Image-to-Image Translation with Conditional Adversarial Networks

phillipi/pix2pix CVPR 2017

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.

Colorful Image Colorization

richzhang/colorization 28 Mar 2016

We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result.

Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2

baldassarreFe/deep-koalarization 9 Dec 2017

We review some of the most recent approaches to colorize gray-scale images using deep learning methods.

Score-Based Generative Modeling through Stochastic Differential Equations

yang-song/score_sde ICLR 2021

Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.

Image Colorization with Generative Adversarial Networks

ImagingLab/Colorizing-with-GANs 14 Mar 2018

Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images.

Guided Image Generation with Conditional Invertible Neural Networks

VLL-HD/FrEIA 4 Jul 2019

We demonstrate these properties for the tasks of MNIST digit generation and image colorization.

Joint Intensity-Gradient Guided Generative Modeling for Colorization

yqx7150/JGM 28 Dec 2020

Furthermore, the joint intensity-gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, and it is conducive to edge-preserving.

Consistency Models

openai/consistency_models 2 Mar 2023

Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3. 55 on CIFAR-10 and 6. 20 on ImageNet 64x64 for one-step generation.

Wavelet Transform-assisted Adaptive Generative Modeling for Colorization

yqx7150/WACM 9 Jul 2021

Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples.