Colorization
154 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
Libraries
Use these libraries to find Colorization models and implementationsMost implemented papers
Palette: Image-to-Image Diffusion Models
We expect this standardized evaluation protocol to play a role in advancing image-to-image translation research.
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Firstly, we construct a pretext task, \textit{i. e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network.
Learning Representations for Automatic Colorization
This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
Real-Time User-Guided Image Colorization with Learned Deep Priors
The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
Multimodal Generative Models for Scalable Weakly-Supervised Learning
Multiple modalities often co-occur when describing natural phenomena.
Automatic Temporally Coherent Video Colorization
This paper proposes a method to colorize line art frames in an adversarial setting, to create temporally coherent video of large anime by improving existing image to image translation methods.
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
In this paper, we propose an adversarial learning colorization approach coupled with semantic information.
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.