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Pre- and post-processes for automatic colorization using a fully convolutional network

Authors:

Yasuhiro MukaigawaAuthors Info & Claims

SA '18: SIGGRAPH Asia 2018 Posters

Article No.: 70, Pages 1 - 2

https://doi.org/10.1145/3283289.3283307

Published: 04 December 2018 Publication History

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Abstract

Automatic colorization is a significant task especially for Anime industry. An original trace image to be colorized contains not only outlines but also boundary contour lines of shadows and highlight areas. Unfortunately, these lines tend to decrease the consistency among all images. Thus, this paper provides a method for a cleaning pre-process of anime dataset to improve the prediction quality of a fully convolutional network, and a refinement post-process to enhance the output of the network.

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Jonathan Long Evan Shelhamer and Trevor Darrell. 2016. Fully Convolutional Network for Semantic Segmentation. In arXiv.

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Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2016. Let There Be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification. ACM Trans. Graph. 35, 4, Article 110 (July 2016), 11 pages.

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Xueting Liu, Tien-Tsin Wong, and Pheng-Ann Heng. 2015. Closure-aware Sketch Simplification. ACM Trans. Graph. 34, 6, Article 168 (Oct. 2015), 10 pages.

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Taizan Yonetsuji. 2017. PaintsChainer. github.com/pfnet/PaintsChainer

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Richard Zhang, Phillip Isola, and Alexei A Efros. 2016. Colorful Image Colorization. In ECCV.

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Cited By

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Vidyalakshmi RKathirvel NGayathri NBhuvaneshvari KAarthi MSivaramKrishnan M(2024)Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System2024 5th International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC61587.2024.10722301(1873-1877)Online publication date: 18-Sep-2024
https://doi.org/10.1109/ICOSEC61587.2024.10722301
Maejima AShinagawa SKubo HFunatomi TYotsukura TNakamura SMukaigawa Y(2024)Continual few-shot patch-based learning for anime-style colorizationComputational Visual Media10.1007/s41095-024-0414-410:4(705-723)Online publication date: 9-Jul-2024
https://doi.org/10.1007/s41095-024-0414-4
Maejima AKubo HShinagawa SFunatomi TYotsukura TNakamura SMukaigawa Y(2021)Anime Character Colorization using Few-shot LearningSIGGRAPH Asia 2021 Technical Communications10.1145/3478512.3488604(1-4)Online publication date: 14-Dec-2021
https://doi.org/10.1145/3478512.3488604
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Index Terms

Pre- and post-processes for automatic colorization using a fully convolutional network
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation

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Information & Contributors

Information

Published In

SA '18: SIGGRAPH Asia 2018 Posters

December 2018

166 pages

ISBN:9781450360630

DOI:10.1145/3283289

Conference Chairs:
Nafees Bin Zafar
Magic Leap
,
Kun Zhou
Zhejiang University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2018

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SA '18

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SIGGRAPH

SA '18: SIGGRAPH Asia 2018

December 4 - 7, 2018

Tokyo, Japan

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

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Vidyalakshmi RKathirvel NGayathri NBhuvaneshvari KAarthi MSivaramKrishnan M(2024)Design and Evaluation of Generative Adversarial Network for Automated Image Colorization System2024 5th International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC61587.2024.10722301(1873-1877)Online publication date: 18-Sep-2024
https://doi.org/10.1109/ICOSEC61587.2024.10722301
Maejima AShinagawa SKubo HFunatomi TYotsukura TNakamura SMukaigawa Y(2024)Continual few-shot patch-based learning for anime-style colorizationComputational Visual Media10.1007/s41095-024-0414-410:4(705-723)Online publication date: 9-Jul-2024
https://doi.org/10.1007/s41095-024-0414-4
Maejima AKubo HShinagawa SFunatomi TYotsukura TNakamura SMukaigawa Y(2021)Anime Character Colorization using Few-shot LearningSIGGRAPH Asia 2021 Technical Communications10.1145/3478512.3488604(1-4)Online publication date: 14-Dec-2021
https://doi.org/10.1145/3478512.3488604
Ishii DKubo HShinagawa SMaejima AFunatomi TNakamura SMukaigawa Y(2020)Confidence-aware Practical Anime-style ColorizationACM SIGGRAPH 2020 Talks10.1145/3388767.3407331(1-2)Online publication date: 17-Aug-2020
https://dl.acm.org/doi/10.1145/3388767.3407331
Maejima AKubo HFunatomi TYotsukura TNakamura SMukaigawa Y(2019)Graph matching based anime colorization with multiple referencesACM SIGGRAPH 2019 Posters10.1145/3306214.3338560(1-2)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3306214.3338560

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Improved Automatic Colorization by Optimal Pre-colorization

Colorization of grayscale images and videos using a semi-automatic approach

Automatic Cartoon Colorization Based on Convolutional Neural Network