Application of multi-level adaptive neural network based on optimization algorithm in image style transfer
Arbitrary image style transfer is the process of inputting any set of images to generate images with a certain artistic style. Aiming at the problem of how to adapt both global style and local style and maintain spatial consistency based on the arbitrary style transfer algorithm. This paper proposed...
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| Vydáno v: | Multimedia tools and applications Ročník 83; číslo 29; s. 73127 - 73149 |
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01.09.2024
Springer Nature B.V |
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| ISSN: | 1573-7721, 1380-7501, 1573-7721 |
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| Abstract | Arbitrary image style transfer is the process of inputting any set of images to generate images with a certain artistic style. Aiming at the problem of how to adapt both global style and local style and maintain spatial consistency based on the arbitrary style transfer algorithm. This paper proposed a multi-level adaptive arbitrary style transfer network and adopted a multi-level strategy to integrate multi-level context information in a progressive manner. First, the convolution block attention module
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is referenced to the encoder to improve the semantic matching of the algorithm and maintain spatial consistency. Secondly, the multi-branch content is integrated with the style features, quantifying the local similarity between content and style features in a non-local way, rearranges the distribution of style representation according to the content representation. Finally, the multi-layer features after alignment are provided to the decoder module by the Adaptive Weight Skip Connection
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, which can integrate local and global styles efficiently and flexibly. In addition, the identity loss is used to eliminate image artifacts and better retain the content structure information. Qualitative and quantitative experiments show that the proposed method is superior to the most advanced CNN-based method, and can generate high-quality stylized images with arbitrary styles and better visual effects. |
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| AbstractList | Arbitrary image style transfer is the process of inputting any set of images to generate images with a certain artistic style. Aiming at the problem of how to adapt both global style and local style and maintain spatial consistency based on the arbitrary style transfer algorithm. This paper proposed a multi-level adaptive arbitrary style transfer network and adopted a multi-level strategy to integrate multi-level context information in a progressive manner. First, the convolution block attention module
C
B
A
M
is referenced to the encoder to improve the semantic matching of the algorithm and maintain spatial consistency. Secondly, the multi-branch content is integrated with the style features, quantifying the local similarity between content and style features in a non-local way, rearranges the distribution of style representation according to the content representation. Finally, the multi-layer features after alignment are provided to the decoder module by the Adaptive Weight Skip Connection
A
W
S
C
, which can integrate local and global styles efficiently and flexibly. In addition, the identity loss is used to eliminate image artifacts and better retain the content structure information. Qualitative and quantitative experiments show that the proposed method is superior to the most advanced CNN-based method, and can generate high-quality stylized images with arbitrary styles and better visual effects. Arbitrary image style transfer is the process of inputting any set of images to generate images with a certain artistic style. Aiming at the problem of how to adapt both global style and local style and maintain spatial consistency based on the arbitrary style transfer algorithm. This paper proposed a multi-level adaptive arbitrary style transfer network and adopted a multi-level strategy to integrate multi-level context information in a progressive manner. First, the convolution block attention module CBAM is referenced to the encoder to improve the semantic matching of the algorithm and maintain spatial consistency. Secondly, the multi-branch content is integrated with the style features, quantifying the local similarity between content and style features in a non-local way, rearranges the distribution of style representation according to the content representation. Finally, the multi-layer features after alignment are provided to the decoder module by the Adaptive Weight Skip Connection AWSC, which can integrate local and global styles efficiently and flexibly. In addition, the identity loss is used to eliminate image artifacts and better retain the content structure information. Qualitative and quantitative experiments show that the proposed method is superior to the most advanced CNN-based method, and can generate high-quality stylized images with arbitrary styles and better visual effects. |
| Author | Li, Hong-an Wang, Lanye Liu, Jun |
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| Cites_doi | 10.1109/MCG.2003.1210863 10.1145/3394171.3414015 10.1007/978-3-319-10602-1_48 10.1609/aaai.v34i04.5862 10.1109/ICCV48922.2021.01435 10.3934/mbe.2021330 10.1007/978-3-319-46475-6_43 10.1109/CVPR.2017.759 10.1109/CVPR.2019.00603 10.1609/aaai.v35i2.16208 10.1109/CVPR.2017.296 10.1109/CVPR.2018.00745 10.1155/2022/1693892 10.1109/CVPR.2018.00860 10.1109/CVPR.2017.36 10.1109/CVPR46437.2021.00092 10.1109/CVPR.2018.00858 10.1109/CVPR.2016.272 10.1109/CVPR.2016.265 10.1109/CVPR.2017.437 10.1109/ICCV.2017.167 10.1109/ICCV48922.2021.00658 10.1007/978-3-030-01234-2_1 10.13232/j.cnki.jnju.2021.01.001 |
| ContentType | Journal Article |
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| Keywords | Arbitrary image style transfer Convolution block attention module Multi-level strategy Adaptive weight skip connection |
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| References_xml | – reference: Wang X, Oxholm G, Zhang D et al (2017) Multimodal transfer: a hierarchical deep convolutional neural network for fast artistic style transfer. Proc of IEEE conference on computer vision and pattern recognition. Washington DC: IEEE Computer Society, 7178–7186 – reference: Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2021) The unreasonable effectiveness of deep features as a perceptual metric. IEEE, pp 1–10 – reference: Ulyanov D, Lebedev V, Vedaldi A et al (2016) Texture networks: feed-forward synthesis of textures and stylized images. Proceedings of the international conference on machine learning(ICML), 1349–1357 – reference: Jacobs C, Salesin D, Oliver N, Hertzmann A, Curless AB (2001)Image analogies. Proc Siggraph, pp 327–340 – reference: Lee KH, Park DY (2019) Arbitrary style transfer with style-attentional networks. 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