Colorization
157 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
Finite Scalar Quantization: VQ-VAE Made Simple
google-research/google-research
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Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets.
The Fast Bilateral Solver
We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms.
Infrared Colorization Using Deep Convolutional Neural Networks
This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks.
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.
SketchyScene: Richly-Annotated Scene Sketches
SketchyScene/SketchyScene
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ECCV 2018
We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level.
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
orashi/AlacGAN
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Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization.
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
blandocs/Tag2Pix
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ICCV 2019
A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image.
Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System
bupt-wx/Deep-Convolutional-Network-For-Colorization-In-Monochrome-Color-Dual-Lens-System
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Proceedings of the AAAI Conference on Artificial Intelligence 2019
To get high-quality color images, it is desired to colorize the gray image with the color image as reference.
ColorFool: Semantic Adversarial Colorization
smartcameras/ColorFool
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CVPR 2020
Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers.
Instance-aware Image Colorization
ericsujw/InstColorization
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CVPR 2020
Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly.
