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
Blind Video Temporal Consistency via Deep Video Prior
ChenyangLEI/deep-video-prior
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NeurIPS 2020
Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency.
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
Our method, though partly reliant on the quality of the generative network inversion, is competitive with state-of-the-art supervised and task-specific restoration methods.
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.
Generative Modeling with Optimal Transport Maps
LituRout/OptimalTransportModeling
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ICLR 2022
In particular, we consider denoising, colorization, and inpainting, where the optimality of the restoration map is a desired attribute, since the output (restored) image is expected to be close to the input (degraded) one.
Improving Diffusion Models for Inverse Problems using Manifold Constraints
Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process.
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
We consider the ubiquitous linear inverse problems with additive Gaussian noise and propose an unsupervised sampling approach called diffusion model based posterior sampling (DMPS) to reconstruct the unknown signal from noisy linear measurements.
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.
ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization
To explore this property for better spatial and temporal feature utilization, we develop a local attention module to aggregate the features from adjacent frames in a spatial-temporal neighborhood.
Regularized Discrete Optimal Transport
The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes.
Deep Colorization
djflstkddk/Auto-Colorization
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ICCV 2015
This paper investigates into the colorization problem which converts a grayscale image to a colorful version.
