Colorization
156 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
Benchmarks
These leaderboards are used to track progress in Colorization
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Libraries
Use these libraries to find Colorization models and implementations
SPair-71k
ImageNet ctest10k
SketchyScene
NCD
QST
AnimeCeleb
Most implemented papers
Image-to-Image Translation with Conditional Adversarial Networks
phillipi/pix2pix
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CVPR 2017
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
Colorful Image Colorization
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
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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
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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.
Consistency Models
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.
Image Colorization with Generative Adversarial Networks
ImagingLab/Colorizing-with-GANs
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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
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We demonstrate these properties for the tasks of MNIST digit generation and image colorization.
Joint Intensity-Gradient Guided Generative Modeling for Colorization
yqx7150/JGM
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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.
Multimodal Generative Models for Scalable Weakly-Supervised Learning
mhw32/multimodal-vae-public
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NeurIPS 2018
Multiple modalities often co-occur when describing natural phenomena.
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
In this paper, we propose an adversarial learning colorization approach coupled with semantic information.
