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COMPARISON OF PSNR EVALUATION RESULTS
| Image generation network | SSIM |
|---|---|
| The method of this paper | 88.47% |
| A generation model with nine residual blocks | 81.65% |
| A generating model with six residual blocks | 76.31% |
DISCRIMINATOR MODEL NETWORK STRUCTURE PARAMETER TABLE
| Inputs | Type | Kernel | Batch Normalization | Activation Function | Outputs |
|---|---|---|---|---|---|
| 256x256 | conv | 4x4 | YES | LeakyReLU | 128x128 |
| 128x128 | conv | 4x4 | YES | LeakyReLU | 64x64 |
| 64x64 | conv | 4x4 | YES | LeakyReLU | 32x32 |
| 32x32 | conv | 4x4 | YES | LeakyReLU | 31x31 |
| 31x31 | conv | 4x4 | YES | LeakyReLU | 30x30 |
EXPERIMENTAL ENVIRONMENT
| Operating system | Ubuntu 18.04 LTS 64bit |
|---|---|
| CPU | Intel(R )Xeon(R) Gold 5118 CPU@2.30GHz |
| GPU | Nvidia GeForece TITAN Xp |
| Memory | 32G |
| programing language | Python3.6.1 |
| compiler | Pycharm2018.3 |
| Deep learning framework | pytorch 0.4 |
GENERATOR MODEL NETWORK STRUCTURE PARAMETER TABLE
| Inputs | Type | Kernel | Batch Normalization | Activation Function | Outputs |
|---|---|---|---|---|---|
| 256x256 | conv | 4x4 | YES | RELU | 128x128 |
| 128x128 | conv | 4x4 | YES | RELU | 64x64 |
| 64x64 | conv | 4x4 | YES | RELU | 32x32 |
| 32x32 | conv | 4x4 | YES | RELU | 16x16 |
| 16x16 | conv | 4x4 | YES | RELU | 8x8 |
| 8x8 | conv | 4x4 | YES | RELU | 4x4 |
| 4x4 | conv | 4x4 | YES | RELU | 2x2 |
| 2x2 | conv | 4x4 | YES | RELU | 1x1 |
| 1x1 | deconv | 4x4 | YES | RELU | 2x2 |
| 2x2 | deconv | 4x4 | YES | RELU | 4x4 |
| 4x4 | deconv | 4x4 | YES | RELU | 8x8 |
| 8x8 | deconv | 4x4 | YES | RELU | 16x16 |
| 16x16 | deconv | 4x4 | YES | RELU | 32x32 |
| 32x32 | deconv | 4x4 | YES | RELU | 64x64 |
| 64x64 | deconv | 4x4 | YES | RELU | 128x128 |
| 128x128 | deconv | 4x4 | YES | RELU | 256x256 |