Image Colorization
52 papers with code • 2 benchmarks • 2 datasets
ImageNet
CelebA
Most implemented papers
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
wyhuai/ddnm
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Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
Instance-aware Image Colorization
ericsujw/InstColorization
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CVPR 2020
Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly.
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.
Learning Diverse Image Colorization
aditya12agd5/divcolor
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CVPR 2017
Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings.
Probabilistic Image Colorization
ameroyer/PIC
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We develop a probabilistic technique for colorizing grayscale natural images.
Language-Based Image Editing with Recurrent Attentive Models
First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system.
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
awesome-davian/Text2Colors
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ECCV 2018
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette.
Image Processing Using Multi-Code GAN Prior
genforce/mganprior
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CVPR 2020
Such an over-parameterization of the latent space significantly improves the image reconstruction quality, outperforming existing competitors.
