Research on Colorization of Qinghai Farmer Painting Image Based on Generative Adversarial Networks
Abstract
At present, deep learning method is widely used in the field of gray image colorization. Qinghai farmer painting has distinct national characteristics. The farmer painting has bright colors, high saturation, chaotic color distribution and low color contrast, so it is difficult to restore the image color with high fidelity by using the general deep learning image colorization method. The Pix2Pix generation adversarial network of grayscale image colorization method uses the Leaky ReLU function as the activation function. The proposal algorithm replaces the maximum pooling layer with the convolution layer to retain more image feature information and further to improve the color simulation effect. Meanwhile, in view of the lack of relevant Qinghai farmer painting data set, the data set of Qinghai farmer paintings is constructed to meet the needs of network training. The experimental results show that the improved method further improves the color effect and can generate high quality color images of Qinghai farmer paintings with more real color distribution.
References
[1]
Su J W, Chu H K, Huang J B. 2020. Instance-aware image colorization. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Preprint]. Available at: https://doi.org/10.1109/cvpr42600.2020.00799.
[2]
Žeger I, Grgic S, Vuković J, 2021. Grayscale image colorization methods: Overview and evaluation. IEEE Access, 9, pp. 113326–113346. Available at: https://doi.org/10.1109/access.2021.3104515.
[3]
Lee J, Kim E, Lee Y, 2020. Reference-based sketch image colorization using augmented-self reference and dense semantic correspondence. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Preprint]. Available at: https://doi.org/10.1109/cvpr42600.2020.00584.
[4]
Casaca W, Colnago M, Nonato L G. 2015. Interactive image colorization using Laplacian coordinates. Computer Analysis of Images and Patterns, pp. 675–686. Available at: https://doi.org/10.1007/978-3-319-23117-4_58.
[5]
LEVIN A, LISCHINSKI D, WEISS Y. 2004. Colorization using optimization,” ACM SIGGRAPH 2004 Papers [Preprint]. Available at: https://doi.org/10.1145/1186562.1015780.
[6]
Huang Y C, Tung Y S, Chen J C, 2005. An adaptive edge detection based colorization algorithm and its applications. Proceedings of the 13th annual ACM international conference on Multimedia [Preprint]. Available at: https://doi.org/10.1145/1101149.1101223.
[7]
Yatziv L, Sapiro G. 2006. Fast image and video colorization using chrominance blending. IEEE Transactions on Image Processing, 15(5), pp. 1120–1129. Available at: https://doi.org/10.1109/tip.2005.864231.
[8]
Qu Y, Wong T T, Heng P A. 2006. Manga colorization. ACM SIGGRAPH 2006 Papers on - SIGGRAPH '06 [Preprint]. Available at: https://doi.org/10.1145/1179352.1142017.
[9]
Luan Q, Wen F, Cohen-Or D, 2007. Natural image colorization. Proceedings of the 18th Eurographics conference on Rendering Techniques. 2007: 309-320.
[10]
HEU J H, HYUN D Y, KIM C S, 2009. Image and video colorization based on prioritized source propagation. 2009 16th IEEE International Conference on Image Processing (ICIP) [Preprint]. Available at: https://doi.org/10.1109/icip.2009.5414371.
[11]
Welsh T, Ashikhmin M, Mueller K. 2002. Transferring color to greyscale images. Proceedings of the 29th annual conference on Computer graphics and interactive techniques [Preprint]. Available at: https://doi.org/10.1145/566570.566576.
[12]
Li B, Zhao F, Su Z, 2017. Example-based image colorization using locality consistent sparse representation. IEEE Transactions on Image Processing, 26(11), pp. 5188–5202. Available at: https://doi.org/10.1109/tip.2017.2732239.
[13]
Charpiat G, Hofmann M, Schölkopf B. 2008. Automatic image colorization via multimodal predictions. Lecture Notes in Computer Science, pp. 126–139. Available at: https://doi.org/10.1007/978-3-540-88690-7_10.
[14]
Li B, Lai Y K, John M, 2019. Automatic example-based image colorization using location-aware cross-scale matching. IEEE Transactions on Image Processing, 28(9), pp. 4606–4619. Available at: https://doi.org/10.1109/tip.2019.2912291.
[15]
Cao Liqin, Shang Yongxing, Liu Tingting, Li Zhijiang, Ma Ailong. 2019. Locally Adaptive Grayscale Image Colorization. Chinese Journal of Image Graphics. 24(08): 1249-1257.
[16]
Cortes C, Vapnik V. 1995. Support-vector networks. Machine learning. 20(3): 273-297.
[17]
Achanta R, Shaji A, Smith K, 2012. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(11), pp. 2274–2282. Available at: https://doi.org/10.1109/tpami.2012.120.
[18]
Goodfellow I J, Pouget-Abadie J, Mirza M, 2014. Generative Adversarial Networks. Cambridge University Press Ebooks. 153–173. https://doi.org/10.1017/9781108891530.013
[19]
Han L, Min M R, Stathopoulos A, 2021. Dual Projection Generative Adversarial Networks for Conditional Image Generation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.01417
[20]
Nazeri K, Ng E, Ebrahimi M. 2018. Image Colorization Using Generative Adversarial Networks. Lecture Notes in Computer Science. 85–94. https://doi.org/10.1007/978-3-319-94544-6_9
[21]
Liu Changtong, Cao Lin, Du Kangning. 2020. Portrait Coloring Based on Joint Consistent Cyclic Generative Adversarial Networks. Computer Engineering and Applications. 56(16): 183-190.
[22]
Zhu J Y, Park T, Isola P, 2017. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. International Conference on Computer Vision. https://doi.org/10.1109/iccv.2017.244
[23]
Liang W, Ding D, Wei G. 2021. An improved DualGAN for near-infrared image colorization. Infrared Physics & Technology. 116, 103764. https://doi.org/10.1016/j.infrared.2021.103764
[24]
Yi Z, Zhang H, Tan P, 2017. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation. ArXiv (Cornell University). https://doi.org/10.1109/iccv.2017.310
[25]
Zhao Y, Po L M, Yu W Y, 2022. VCGAN: Video Colorization with Hybrid Generative Adversarial Network. IEEE Transactions on Multimedia, 1. https://doi.org/10.1109/tmm.2022.3154600
[26]
He K, Zhang X, Ren S, 2016. Deep Residual Learning for Image Recognition. Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2016.90
[27]
Zhang Yi, Wei Wenwen, Gong Zhiyuan. 2021. Gray-scale image colorization method based on deep aggregate structure network. Computer Application Research. 38(03): 923-927.
[28]
Wu Lidan, Xue Yuyang, Tong Tong, Du Min, Gao Qinquan. 2021. Image Coloring Algorithm Based on Foreground Semantic Information. Computer Applications. 41(07): 2048-2053.
[29]
Wan Yuanyuan, Wang Yuqing, Zhang Xiaoning, Li Yuqun, Chen Xiaolin. 2021. Adversarial grayscale image colorization combined with global semantic optimization. Yejing Yu Xianshi. https://doi.org/10.37188/cjlcd.2021-0012
[30]
Isola P, Zhu J Y, Zhou T, 2017. Image-to-Image Translation with Conditional Adversarial Networks. Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2017.632
[31]
Li C, Wand M. 2016. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. Lecture Notes in Computer Science. 702–716. https://doi.org/10.1007/978-3-319-46487-9_43
Index Terms
- Research on Colorization of Qinghai Farmer Painting Image Based on Generative Adversarial Networks
Index terms have been assigned to the content through auto-classification.
Recommendations
PalGAN: Image Colorization with Palette Generative Adversarial Networks
Computer Vision – ECCV 2022AbstractMultimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the ...
Isoline Based Image Colorization
UKSIM '14: Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and SimulationColorization is a process of adding colors to black and white images and videos. Each scalar pixel value is replaced by three values which describe, depending on color mode, e.g. The luminance and the chrominance or red-green-blue components. By ...
A Novel Approach for Colorization of a Grayscale Image using Soft Computing Techniques
Colorization of grayscale image is a process to convert a grayscale image into a color one. Few research works reported in literature on this but there is hardly any generalized method that successfully colorizes all types of grayscale image. This study ...
Comments
Information & Contributors
Information
Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- ??????????????
- ????????????
- ??????????
Conference
CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
Acceptance Rates
CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)20
- Downloads (Last 6 weeks)2
Reflects downloads up to 15 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format