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Research on AIGC empowering digital cultural and creative design style transfer and diversified generation methods

Ran Jia

Ran Jia

Beijing Union UniversityBeijing, China
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Jia, Ran
  
24. März 2025
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Online veröffentlicht: 24. März 2025
Eingereicht: 12. Nov. 2024
Akzeptiert: 13. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0791
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© 2025 Ran Jia, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

VGG 19 network
VGG 19 network

Figure 2.

Schematic diagram of the calculation of the multinomial self-attention
Schematic diagram of the calculation of the multinomial self-attention

Figure 3.

Diagram of the overall network structure
Diagram of the overall network structure

Figure 4.

Training process loss function
Training process loss function

Style transfer experiment evaluation results

Style transfer Method PSNR/dB MSE MS-SSIM/dB Per-pixel acc
Original image→cartoon style AdaIN 20.304 87.851 0.871 0.040
SANet 19.801 85.228 0.919 0.041
StyTr2 20.181 88.609 0.921 0.051
AdaAttN 20.436 87.892 0.887 0.064
CycleGAN 19.778 90.999 0.898 0.067
Ours 21.293 82.678 0.939 0.071
Original image→oil painting style AdaIN 17.841 84.445 0.836 0.576
SANet 17.477 84.234 0.832 0.477
StyTr2 17.583 83.689 0.843 0.524
AdaAttN 17.552 83.709 0.842 0.608
CycleGAN 17.635 83.275 0.838 0.419
Ours 17.857 81.808 0.854 0.723
Original image→new Chinese style AdaIN 22.882 73.425 0.951 0.088
SANet 23.214 72.737 0.946 0.106
StyTr2 22.833 72.887 0.959 0.116
AdaAttN 22.874 74.443 0.941 0.155
CycleGAN 22.956 74.198 0.955 0.142
Ours 24.306 71.243 0.962 0.179

Application of evaluation index weight synthetic distribution in improved CycleGAN

Primary index Weight Secondary index Weight Synthetic weight
Style transfer effect evaluation system Image quality (A) 0.3758 Image fidelity (A1) 0.1274 0.0479
Detail accuracy (A2) 0.1820 0.0684
Image diversity (A3) 0.1136 0.0427
Image expression (A4) 0.1538 0.0578
Image innovation (A5) 0.1243 0.0467
Image art (A6) 0.1548 0.0582
Image practicality (A7) 0.1441 0.0542
Style simulation (B) 0.3815 Style transfer coordination (B1) 0.2639 0.1007
Style transfer speciality (B2) 0.2213 0.0844
Style transfer applicability (B3) 0.2847 0.1086
Style fusion rationality (B4) 0.2301 0.0878
Connotation expression (C) 0.2427 Connotation expression form (C1) 0.1745 0.0423
Connotation art (C2) 0.2942 0.0714
Cultural expression (C3) 0.2736 0.0664
Historical expression (C4) 0.2577 0.0625

Application evaluation index system of improved CycleGAN model

Primary index Secondary index
Style transfer effect evaluation system Image quality (A) Image fidelity (A1)
Detail accuracy (A2)
Image diversity (A3)
Image expression (A4)
Image innovation (A5)
Image art (A6)
Image practicality (A7)
Style simulation (B) Style transfer coordination (B1)
Style transfer speciality (B2)
Style transfer applicability (B3)
Style fusion rationality (B4)
Connotation expression (C) Connotation expression form (C1)
Connotation art (C2)
Cultural expression (C3)
Historical expression (C4)

Weight calculation of application evaluation score in improved CycleGAN

Primary index Secondary index Synthetic weight Experimental participant
1 2 3 4 5 6
Image quality (A) A1 0.0479 9 8 9 8 8 9
A2 0.0684 9 8 9 9 8 8
A3 0.0427 8 8 9 8 9 9
A4 0.0578 9 9 9 9 9 8
A5 0.0467 10 10 9 10 9 9
A6 0.0582 9 9 10 10 9 8
A7 0.0542 10 10 10 10 9 10
Style simulation (B) B1 0.1007 10 9 10 8 8 8
B2 0.0844 9 10 9 8 8 8
B3 0.1086 9 9 9 9 8 9
B4 0.0878 9 10 9 10 8 9
Connotation expression (C) C1 0.0423 8 9 10 8 9 9
C2 0.0714 9 8 8 10 9 9
C3 0.0664 9 10 8 9 9 9
C4 0.0625 9 8 9 10 9 9
Decimal score 9.1166 9.0466 9.1176 9.0628 8.5022 8.6847
Centesimal score 91.17 90.47 91.18 90.63 85.02 86.85
Overall evaluation average score 89.22
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