Image Colorization
59 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Image Colorization
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
ImageNet
CelebA
NIR2RGB VCIP Challange Dataset
Most implemented papers
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
•
•
ICCV 2017
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
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
•
•
We review some of the most recent approaches to colorize gray-scale images using deep learning methods.
Image Colorization with Generative Adversarial Networks
ImagingLab/Colorizing-with-GANs
•
•
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
•
•
We demonstrate these properties for the tasks of MNIST digit generation and image colorization.
Composer: Creative and Controllable Image Synthesis with Composable Conditions
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
yqx7150/WACM
•
•
Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples.
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
wyhuai/ddnm
•
•
Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
Learning Representations for Automatic Colorization
This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
kainoj/colnet
•
•
ACM Transactions on Graphics 2016
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
