Digital Generation Technology for Tomb Murals Based on Multiscale Cascade Network

Table 1. Model convergence under different values
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Table 1. Model convergence under different values
$χ_{1}$ $χ_{2}$ Epoch
0.7 0.3 188
0.5 0.5 166
0.4 0.6 143
0.3 0.7 133
0.2 0.8 110
0.1 0.9 Non convergence
0.001 0.999 Non convergence

Table 2. Analysis of generation results between different layers
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Table 2. Analysis of generation results between different layers
Location of self attention mechanism SSIM PSNR /dB
2-3-layer 0.74 31.02
3-4-layer 0.84 31.86
4-5-layer 0.69 20.09

Table 3. Network and loss group name
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Table 3. Network and loss group name
Name Network
$E D_1$ $E D + L_{r e c} + L_{a d v}$
$E D_2$ $E D + L_{r e c} + L_{a d v} + L_{t e x t u r e - l o s s}$
$E D_3$ $E D + L_{r e c} + L_{a d v} + L_{t e x}$
$E D_4$ $E D + L_{r e c} + L_{a d v} + L_{t e x} + s e l f - a t t e n t i o n$

Table 4. Comparison of generated by different methods

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Table 4. Comparison of generated by different methods

Method	Wood block		Mountain block		Tomb passage block
Method	PSNR /dB	SSIM	PSNR /dB	SSIM	PSNR /dB	SSIM
ED_1	25.29	0.69	27.61	0.62	27.86	0.70
ED_2	28.23	0.73	29.69	0.71	30.40	0.77
ED_3	30.66	0.80	31.74	0.83	31.91	0.82
ED_4	31.03	0.85	32.31	0.86	32.82	0.88

Table 5. Comparison of generation results of different algorithms

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Table 5. Comparison of generation results of different algorithms

Method	Horse block		Stone block		Tomb passage block		Wood block
Method	PSNR /dB	SSIM	PSNR /dB	SSIM	PSNR /dB	SSIM	PSNR /dB	SSIM
WGAN-GP	27.35	0.68	24.87	0.57	26.36	0.65	27.85	0.64
DCGAN	30.16	0.78	29.69	0.72	31.59	0.80	29.47	0.72
Proposed	32.66	0.84	31.74	0.80	32.91	0.88	31.66	0.84

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Meng Wu, Yi Ren, Jia Wang. Digital Generation Technology for Tomb Murals Based on Multiscale Cascade Network[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0610002

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