Colorization
79 papers with code • 0 benchmarks • 3 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
SPair-71k
SketchyScene
NCD
Greatest papers with code
Colorization Transformer
google-research/google-research
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ICLR 2021
We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention.
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.
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.
Ranked #80 on
Self-Supervised Image Classification
on ImageNet
Real-Time User-Guided Image Colorization with Learned Deep Priors
junyanz/interactive-deep-colorization
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The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.
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.
Deep Exemplar-based Colorization
msracver/Deep-Exemplar-based-Colorization
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More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references.
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.
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.
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.
Ranked #3 on
Image Generation
on CIFAR-10
