Structural Refinement of Neural Style Transfer

Yu SHEN; Qian YANG; Xiaopeng CHEN; Yubin YUAN; Hongguo ZHANG; Lin WANG

doi:10.11999/JEIT200211

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2361-2369

Yu SHEN, Qian YANG, Xiaopeng CHEN, Yubin YUAN, Hongguo ZHANG, Lin WANG. Structural Refinement of Neural Style Transfer[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2361-2369. doi: 10.11999/JEIT200211

Citation:

Yu SHEN, Qian YANG, Xiaopeng CHEN, Yubin YUAN, Hongguo ZHANG, Lin WANG. Structural Refinement of Neural Style Transfer[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2361-2369. doi: 10.11999/JEIT200211

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Structural Refinement of Neural Style Transfer

doi: 10.11999/JEIT200211

College of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Funds: The National Natural Science Foundation of China (61861025)

Received Date: 2020-03-25
Rev Recd Date: 2021-01-30

Available Online: 2021-07-21

Publish Date: 2021-08-10

Abstract

Abstract

In the process of style transfer, stylized image details are blurred when style elements are evenly distributed in the whole image. Besides, the existing style transfer methods mainly focus on the diversity of transferred styles, ignoring the content structure and details of the stylized images. To this end, a neural style transfer method of structure refinement is proposed, which refines the content structure of stylized image by adding edge detection network to extract the contour edge of the content image to highlight the main objectives in the content image. By replacing the larger convolution kernel of the conventional convolution layer in the transfer network, the model parameters of the transfer network are reduced, and the transfer speed is improved, while ensuring that the original receptive field is unchanged. Through the adaptive normalization of the conventional convolution layer, the structure of the generated image is refined by using the adaptive normalization to detect certain style of stroke in the feature channel to produce high nonlinearity while preserving the spatial structure of the content image. The method can refine the overall structure of the stylized image, make the stylized image more coherent, that the stylized image details are blurred due to the uniform distribution of style texture, and improve the quality of image style transfer.
- Image processing,
- Deep learning,
- Neural network,
- Style transfer,
- Edge detection,
- Normalization

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References(18)

References

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