GAN‐based tone curve learning for colour transfer
A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning...
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| Vydáno v: | Electronics letters Ročník 58; číslo 16; s. 609 - 611 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Stevenage
John Wiley & Sons, Inc
01.08.2022
Wiley |
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| ISSN: | 0013-5194, 1350-911X |
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| Abstract | A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning, which results in unnatural output images when inappropriate images are learned; moreover, in such situations, analysing what kind of colour transformation has actually been performed is difficult. This state of the art motivates the proposal of a new colour transfer method that estimates tone curves based on generative adversarial nets. This method does not require any data set other than input and reference images, thus enabling a more appropriate colour transfer. The superior output of the proposed method compared with some baseline approaches is demonstrated through experiments. |
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| AbstractList | A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning, which results in unnatural output images when inappropriate images are learned; moreover, in such situations, analysing what kind of colour transformation has actually been performed is difficult. This state of the art motivates the proposal of a new colour transfer method that estimates tone curves based on generative adversarial nets. This method does not require any data set other than input and reference images, thus enabling a more appropriate colour transfer. The superior output of the proposed method compared with some baseline approaches is demonstrated through experiments. A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning, which results in unnatural output images when inappropriate images are learned; moreover, in such situations, analysing what kind of colour transformation has actually been performed is difficult. This state of the art motivates the proposal of a new colour transfer method that estimates tone curves based on generative adversarial nets. This method does not require any data set other than input and reference images, thus enabling a more appropriate colour transfer. The superior output of the proposed method compared with some baseline approaches is demonstrated through experiments. Abstract A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning, which results in unnatural output images when inappropriate images are learned; moreover, in such situations, analysing what kind of colour transformation has actually been performed is difficult. This state of the art motivates the proposal of a new colour transfer method that estimates tone curves based on generative adversarial nets. This method does not require any data set other than input and reference images, thus enabling a more appropriate colour transfer. The superior output of the proposed method compared with some baseline approaches is demonstrated through experiments. |
| Author | Uruma, K. Sasaki, R. Ito, D. |
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| Cites_doi | 10.1016/j.cag.2010.11.003 10.1109/ICCV.2019.00913 10.1109/CVPR.2017.740 10.1109/TIP.2020.2989584 10.1007/s00371-020-01921-6 10.1145/1809939.1809949 10.1109/38.946629 10.1109/5289.911175 10.1109/CVPR.2016.265 10.1109/ICCV.2017.167 |
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| Copyright | 2022 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References_xml | – start-page: 9036 year: 2019 end-page: 9045 article-title: Photorealistic style transfer via wavelet transforms – volume: 4 start-page: 44 issue: 1 year: 2001 end-page: 46 article-title: Cubic‐spline interpolation 1 publication-title: IEEE Instrum. Meas. Mag. – volume: 21 start-page: 34 issue: 5 year: 2001 end-page: 41 article-title: Color transfer between images publication-title: IEEE Comput. Graphics Appl. – year: 1980 – year: 2015 article-title: Adam: a method for stochastic optimization – start-page: 2414 year: 2016 end-page: 2423 article-title: Image style transfer using convolutional neural networks – year: 2015 article-title: Unsupervised representation learning with deep convolutional generative adversarial networks – volume: 36 start-page: 2129 issue: 10 year: 2020 end-page: 2143 article-title: Deep color transfer using histogram analogy publication-title: Vis. Comput. – start-page: 81 year: 2010 end-page: 90 – start-page: 2672 year: 2014 end-page: 2680 – start-page: 1501 year: 2017 end-page: 1510 article-title: Arbitrary style transfer in real‐time with adaptive instance normalization – volume: 35 start-page: 67 issue: 1 year: 2011 end-page: 80 article-title: Progressive color transfer for images of arbitrary dynamic range publication-title: Comput. Graphics – start-page: 4990 year: 2017 end-page: 4998 article-title: Deep photo style transfer – volume: 29 start-page: 6223 year: 2020 end-page: 6236 article-title: Personalized image enhancement using neural spline color transforms publication-title: IEEE Trans. Image Process. – ident: e_1_2_10_4_1 doi: 10.1016/j.cag.2010.11.003 – ident: e_1_2_10_8_1 doi: 10.1109/ICCV.2019.00913 – ident: e_1_2_10_7_1 doi: 10.1109/CVPR.2017.740 – ident: e_1_2_10_10_1 doi: 10.1109/TIP.2020.2989584 – volume-title: Elementary Numerical Analysis, An Algorithmic Approach year: 1980 ident: e_1_2_10_13_1 – ident: e_1_2_10_9_1 doi: 10.1007/s00371-020-01921-6 – ident: e_1_2_10_3_1 doi: 10.1145/1809939.1809949 – ident: e_1_2_10_2_1 doi: 10.1109/38.946629 – ident: e_1_2_10_12_1 – ident: e_1_2_10_15_1 – ident: e_1_2_10_14_1 doi: 10.1109/5289.911175 – ident: e_1_2_10_5_1 doi: 10.1109/CVPR.2016.265 – ident: e_1_2_10_6_1 doi: 10.1109/ICCV.2017.167 – start-page: 2672 volume-title: Advances in Neural Information Processing Systems year: 2014 ident: e_1_2_10_11_1 |
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| Snippet | A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs,... Abstract A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs,... |
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| SubjectTerms | Color Deep learning Generative adversarial networks Neural networks Probability distribution Statistical methods |
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| Title | GAN‐based tone curve learning for colour transfer |
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