Single Image Colorization Via Modified Cyclegan

Xiao, Yuxuan; Jiang, Aiwen; Liu, Changhong; Wang, Mingwen

doi:10.1109/icip.2019.8803677

Cited by 17 publications

(4 citation statements)

References 10 publications

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“…It essentially generates a pixel-to-pixel mapped output I (x, y) in the target domain corresponding to input I(x, y) from source domain [41]. This has consequently laid foundation to various vision tasks, like image colourisation [90], conditional image generation [18,38], style-transfer [105], inpainting [34] and enhancements [51,67,105]. Furthermore, pix2pix first illustrated generation of pixel-perfect photos [41] even from sparse line drawings like edgemaps.…”

Section: Related Workmentioning

confidence: 99%

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Koley¹,

Bhunia²,

Sain³

et al. 2023

Preprint

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Edgemap Sketch Sketch Sketch Sketch Sketch Existing Methods Proposed Method Figure 1. (a) Set of photos generated by the proposed method. (b) While existing methods can generate faithful photos from perfectly pixel-aligned edgemaps, they fall short drastically in case of highly deformed and sparse free-hand sketches. In contrast, our autoregressive sketch-to-photo generation model produces highly photorealistic outputs from highly abstract sketches.

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Section: Related Workmentioning

confidence: 99%

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Koley¹,

Bhunia²,

Sain³

et al. 2023

Preprint

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“…They achieved this by training the CNN with various losses. An effective color-CycleGAN solution was proposed by [7] with their objective being to generate reasonable color instead of reinstating the original color and they successfully achieved a performance better than several stat-of-the-art methods. [2] uses the ChromaGAN model which is based on an adversarial strategy that captures perceptual, semantic and geometric information.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Colorization Using Autoencoders

Nihalaani¹

2021

IJRASET

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Modification of art may be viewed as enhancement or vandalization. Even though for a long time many were opposed to the idea of colorizing images, they now have finally viewed it for what it is - an enhancement of the art form. Grayscale image colorization has since been a long-standing artistic division. It has been used to revive or modify images taken prior to the invention of colour photography. This paper explores one method to reinvigorate grayscale images by colorizing them. We propose the use of deep learning, specifically the use of convolution neural networks. The obtained results show the ability of our model to realistically colorize grayscale images. Keywords: Deep Learning, Convolutional Neural Network, Image Colorization, Autoencoders.

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“…Their best results cannot be obtained without the color hint. To this date [10], [11], grey-level image colorization is still an open problem. First, we can have different styles of colorization, which leaves room for different approaches.…”

Section: Introductionmentioning

confidence: 99%

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

Rodrigues

Clua

Vitor

2022

2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

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This work proposes a complete methodology to colorize images of Fakemon, anime-style monster-like creatures.In addition, we propose algorithms to extract the line art from colorized images as well as to extract color hints. Our work is the first in the literature to use automatic color hint extraction, to train the networks specifically with anime-styled creatures and to combine the Pix2Pix and CycleGAN approaches, two different generative adversarial networks that create a single final result. Visual results of the colorizations are feasible but there is still room for improvement.

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Single Image Colorization Via Modified Cyclegan

Cited by 17 publications

References 10 publications

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Image Colorization Using Autoencoders

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

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