<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North and South Dynasties. After convolution and pooling activation, rich and less parametric feature maps are generated to lay a solid foundation for generative adversarial networks. Meanwhile, in order to ensure the rigor of the image dataset of landscape painting images under scene transformation, an improvement is proposed on the basis of traditional GAN technology, and a function represents the improved CycleGAN model. The experimental dataset has been constructed, and the loss function, network training, and hyperparameters have been designed for the model. The visual and conversion outcomes of the model-generated images are evaluated through simulation experiments. The migration of all landscape painting style textures is within the range of (0.5,2.4) at a gray level of 8. The migration styles increase as the gray level increases, and the data between groups does not overlap. In the subjective evaluation of the landscape painting style transformation generated by the model used in this paper, the score of the generated image is 3.8135 points, while the number of high scores is more than 65% of the votes. The model constructed in this paper has a better effect on the transformation of the image style of the Wei, Jin, and North and South Dynasties, and it can satisfy people’s visual needs.</p>
</abstract>

In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North and South Dynasties. After convolution and pooling activation, rich and less parametric feature maps are generated to lay a solid foundation for generative adversarial networks. Meanwhile, in order to ensure the rigor of the image dataset of landscape painting images under scene transformation, an improvement is proposed on the basis of traditional GAN technology, and a function represents the improved CycleGAN model. The experimental dataset has been constructed, and the loss function, network training, and hyperparameters have been designed for the model. The visual and conversion outcomes of the model-generated images are evaluated through simulation experiments. The migration of all landscape painting style textures is within the range of (0.5,2.4) at a gray level of 8. The migration styles increase as the gray level increases, and the data between groups does not overlap. In the subjective evaluation of the landscape painting style transformation generated by the model used in this paper, the score of the generated image is 3.8135 points, while the number of high scores is more than 65% of the votes. The model constructed in this paper has a better effect on the transformation of the image style of the Wei, Jin, and North and South Dynasties, and it can satisfy people’s visual needs.

Deep Learning-Based Image Style Transformation Research on Landscape Paintings of Wei, Jin and North-South Dynasties

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North...

Deep Learning-Based Image Style Transformation Research on Landscape Paintings of Wei, Jin and North-South Dynasties

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 29 mar 2024

Accettato: 17 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2059

Parole chiave
Generative Adversarial Network, CycleGAN model, Loss function, Hyperparameters, Image style conversion

© 2024 Shuangqi Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep Learning-Based Image Style Transformation Research on Landscape Paintings of Wei, Jin and North-South Dynasties

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 29 mar 2024

Accettato: 17 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2059

Parole chiaveGenerative Adversarial Network, CycleGAN model, Loss function, Hyperparameters, Image style conversion

© 2024 Shuangqi Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Generative Adversarial Network, CycleGAN model, Loss function, Hyperparameters, Image style conversion