Colorization
156 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 with no code
Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models
Toon shading is a type of non-photorealistic rendering task of animation.
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain
Automatic colorization of grayscale image has been a challenging task.
Color-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization
This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks.
ColorizeDiffusion: Adjustable Sketch Colorization with Reference Image and Text
Recently, diffusion models have demonstrated their effectiveness in generating extremely high-quality images and have found wide-ranging applications, including automatic sketch colorization.
Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation
To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization.
SPDGAN: A Generative Adversarial Network based on SPD Manifold Learning for Automatic Image Colorization
In this work, we propose a fully automatic colorization approach based on Symmetric Positive Definite (SPD) Manifold Learning with a generative adversarial network (SPDGAN) that improves the quality of the colorization results.
Diffusing Colors: Image Colorization with Text Guided Diffusion
To tackle these issues, we present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts.
IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
Given a textual description of a visual task (e. g. "Left: input image, Right: foreground segmentation"), a few input-output visual examples, or both, the model in-context learns to solve it for a new test input.
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.
