Nonlinear optimization generating the Tomb Mural Blocks by GANS
, , oraz | 31 gru 2020
O artykule
Data publikacji: 31 gru 2020
Zakres stron: 43 - 56
Otrzymano: 25 wrz 2020
Przyjęty: 02 gru 2020
DOI: https://doi.org/10.2478/amns.2020.2.00072
Słowa kluczowe
© 2021 Meng Wu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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The comparison for different GANS
| GANS type | Improvement points | Highlight improvement effect |
|---|---|---|
| f-GAN | Distance metric | The f-divergence metric was used for the discriminator model |
| WGAN | Distance metric | Wasserstein distance measurement is adopted to improve the stability of network training and prevent network crash effectively |
| WGAN-GP | Distance metric | Optimize the objective function of the identification model |
| EB-GAN | Energy model | There are more options for network structure and loss functions |
| PG-GAN | Add incentive convergence | The training efficiency is improved to obtain high-quality and diverse generated images |
| LAP-GAN | Add CGAN layer to the Laplace pyramid | Increase the number of pixels in the resulting image |
| CVAE-GAN | Fused VAE and GAN | Assign a specific label to the image |
| SGAN | Learn both a single generation model and a single semi-supervised classification | The classification efficiency of experimental data set is improved and the training time scheduling is more flexible |
| CGAN | Add additional information to the target function in the network | Customize the type of image generation |
| DCGAN | Add deep convolution layer and deconvolution layer | Recognize more advanced image features to improve network stability performance |
The training data in tomb mural blocks in Crown Prince Zhanghuai
| Block 1 | Block 2 | Block 3 | Block 4 | Block 5 | |
|---|---|---|---|---|---|
| Pixel | 2210*1800 | 2220*1850 | 2270*1290 | 2510*1830 | 2060*2120 |
| Number of blocks | 384 | 367 | 362 | 324 | 387 |
| 4.44 | 5.01 | 2.07 | 4.44 | 5.00 |
The evaluation of the generation results in article [17] and ours
| Evaluation | figure a | figure b | figure c | figure d |
|---|---|---|---|---|
| SSIM value[ |
0.842 | 0.756 | 0.597 | 0.874 |
| SSIM value(ours) | 0.937 | 0.842 | 0.731 | 0.943 |
| PSNR value[ |
26.505 | 24.272 | 23.174 | 25.224 |
| PSNR value (ours) | 34.852 | 31.758 | 28.226 | 31.741 |
Construction parameter list of generator G
| input | filter | kemel | strides | dilation | output | |
|---|---|---|---|---|---|---|
| Conv1 | g_shape | 64 | 4 | 1 | 1 | g1 |
| Conv2 | g1 | 128 | 4 | 2 | 1 | g2 |
| Conv3 | g2 | 256 | 4 | 2 | 1 | g3 |
| Conv4 | g3 | 512 | 4 | 1 | 1 | g4 |
| Conv5 | g4 | 512 | 4 | 1 | 1 | g5 |
| Conv6 | g5 | 512 | 4 | 1 | 2 | g6 |
| Conv7 | g6 | 512 | 4 | 1 | 4 | g7 |
| Conv8 | g7 | 512 | 4 | 1 | 8 | g8 |
| Conv9 | g8 | 512 | 4 | 1 | 16 | g9 |
| Conv10 | g9 | 512 | 4 | 1 | 1 | g10 |
| Conv11 | g10 | 512 | 4 | 1 | 1 | g11 |
| Deconv12 | g11 | 256 | 4 | 2 | 1 | g12 |
| Deconv13 | g12 | 128 | 4 | 2 | 1 | g13 |
| Conv14 | g13 | 128 | 4 | 1 | 1 | g14 |
| Conv15 | g14 | 64 | 4 | 1 | 1 | g15 |
Construction parameter list of discriminator D
| input | filter | kemel | strides | output | |
|---|---|---|---|---|---|
| Conv1 | d_input | 32 | 5 | 2 | d1 |
| Conv2 | d1 | 64 | 5 | 2 | d2 |
| Conv3 | d2 | 64 | 5 | 2 | d3 |
| Conv4 | d3 | 128 | 5 | 2 | d4 |
| Conv5 | d4 | 128 | 5 | 2 | d5 |