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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find Colorization models and implementations
SPair-71k
ImageNet ctest10k
SketchyScene
NCD
QST
AnimeCeleb
Latest papers with no code
A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches
To our knowledge, this is the first attempt to propose a research line for SAR colorization that includes a protocol, a benchmark, and a complete performance evaluation.
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
To this end, and capitalizing on the powerful fine-grained generative control offered by the recent diffusion-based generative models, we introduce Steered Diffusion, a generalized framework for photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation.
Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model
This image manipulation can be used for grayscale satellite, medical and historical images making them more expressive.
CoRF : Colorizing Radiance Fields using Knowledge Distillation
We propose a distillation based method to transfer color knowledge from the colorization networks trained on natural images to the radiance field network.
Histogram-guided Video Colorization Structure with Spatial-Temporal Connection
Video colorization, aiming at obtaining colorful and plausible results from grayish frames, has aroused a lot of interest recently.
Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation
To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i. e., latent spectrum context priors and task domain priors), dubbed CoColor.
Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement
The colorization sub-task is accomplished by regarding the chrominance of the low-light image as color guidance like the user-guide image colorization.
DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models
To address these issues, we propose a new method called DiffColor that leverages the power of pre-trained diffusion models to recover vivid colors conditioned on a prompt text, without any additional inputs.
Line Art Colorization of Fakemon using Generative Adversarial Neural Networks
This work proposes a complete methodology to colorize images of Fakemon, anime-style monster-like creatures.
Video Colorization with Pre-trained Text-to-Image Diffusion Models
In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization.
