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. 2022 Sep 20:2022:4376006.
doi: 10.1155/2022/4376006. eCollection 2022.

Style Transfer of Chinese Art Works Based on Dual Channel Deep Learning Model

Yan Tang  1
Affiliations

Style Transfer of Chinese Art Works Based on Dual Channel Deep Learning Model

Yan Tang. Comput Intell Neurosci. .

Abstract

Aiming at the problems of style loss and lack of content in the style transfer of Chinese art works, this paper puts forward the style transfer technology of Chinese art works based on the dual channel deep learning model. On the basis of clarifying the technical principle of style transfer of art works, the image of art works is controlled and transformed based on the u-net network. The incomplete information in the restored image is filled, and the multiscale classification feature is used to calculate the color feature data items in the image. The sensitivity coefficient of color difference is calculated by using constraints, and the overlapping color discrimination and image segmentation of art images are realized. Poisson image editing is used to constrain the image spatial gradient to realize the style migration of art works. The experimental results show that this method can effectively avoid the problems of content error, distortion, and distortion in the process of art style migration, and has a better style migration effect.

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Conflict of interest statement

The author declares that there are no conflicts of interest regarding this work.

Figures

Figure 1
Figure 1
Flow chart of style transfer of art works.
Figure 2
Figure 2
PigNet network structure.
Figure 3
Figure 3
Schematic diagram of image pixel construction in fusion area.
Figure 4
Figure 4
Style transfer process of art works.
Figure 5
Figure 5
Style transfer of art works style transfer effect of art works (a) Premigration image (b) Post migration image.
Figure 6
Figure 6
Distortion parameters generated by three methods.
Figure 7
Figure 7
Overlap values generated by the three methods.
Figure 8
Figure 8
Gray level parameters output by three methods.
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