Colorization
110 papers with code • 1 benchmarks • 6 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
SPair-71k
SketchyScene
NCD
MHMD
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.
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.
Fully Automatic Video Colorization with Self-Regularization and Diversity
ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity
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CVPR 2019
We present a fully automatic approach to video colorization with self-regularization and diversity.
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.
Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
yqx7150/WACM
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Unsupervised deep learning has recently demonstrated the promise to produce high-quality samples.
Learning Representations for Automatic Colorization
This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation.
