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
Latest papers
SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation
zhaoyuzhi/svcnet
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It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding.
AnimeDiffusion: Anime Face Line Drawing Colorization via Diffusion Models
Through multiple quantitative metrics evaluated on our dataset and a user study, we demonstrate AnimeDiffusion outperforms state-of-the-art GANs-based models for anime face line drawing colorization.
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.
Tuning computer vision models with task rewards
google-research/big_vision
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Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models.
Self-supervised pseudo-colorizing of masked cells
roydenwa/cell-centroid-former
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Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning.
High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets
booooooooooo/cri
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A generic method for generating a high-quality image from the degraded one is in demand.
Accelerating Guided Diffusion Sampling with Splitting Numerical Methods
Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task.
Improving Sketch Colorization using Adversarial Segmentation Consistency
We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function.
DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness.
Generative Colorization of Structured Mobile Web Pages
The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements.
