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
MEM: Multi-Modal Elevation Mapping for Robotics and Learning
leggedrobotics/elevation_mapping_cupy
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Elevation maps are commonly used to represent the environment of mobile robots and are instrumental for locomotion and navigation tasks.
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.
Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
maheyg/swgg
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Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake.
Improving Video Colorization by Test-Time Tuning
indigopurple/t3
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With the advancements in deep learning, video colorization by propagating color information from a colorized reference frame to a monochrome video sequence has been well explored.
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models
hernan0930/Diffusart-CVPRW
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2023
Colorization of line art drawings is an important task in illustration and animation workflows.
Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
gongyeliu/ssd
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Recently, diffusion models have demonstrated a remarkable ability to solve inverse problems in an unsupervised manner.
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
omerb01/puq
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Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
Temporal Consistent Automatic Video Colorization via Semantic Correspondence
bupt-ai-cz/tcvc
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Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively.
Improved Diffusion-based Image Colorization via Piggybacked Models
Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization.
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
viector/spcolor
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Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch.
