Figure 1.
U-Net Network structure
Figure 2.
Residual learning unit RB
Figure 3.
Channel attention (CA)
Figure 4.
Residual channel attention block (RCAB)
Algorithm 1:
Adaptation Algorithm on Unpaired Data
| Input: Unpaired real-world data: Su; |
| Paired synthetic data: Sp; |
| Batch sizes for Su and Sp : x and y; |
| Indicator function: Sm. |
| 1. Load the pretrained models P and u; |
| 2. while not convergent do |
| 3. Sample unlabeled data {xi} from SU; |
| 4. Sample labeled data {(xi, yi)} from SP; |
| 5. // Update the primal mode |
| 6. Update P by minimizing the objective: |
| 7. |
| 8. // Update the dual model |
| 9. Update D by minimizing the objective: |
| 10. |
| 11. END |
Figure 5.
Double regression theoretical model
Figure 6.
A double regression network training model based on U-Net network transformation is presented
Figure 7.
Sample graph of data set
Figure 8.
PSNR data comparison graph
TABLE I.
Ablation experiment
| PSNR | CA | RB | Direct connection path | PReLu |
|---|---|---|---|---|
| 37.850 | No | Yes | Yes | Yes |
| 37.844 | Yes | No | Yes | Yes |
| 37.738 | No | No | Yes | Yes |
| 37.919 | Yes | Yes | No | Yes |
| 37.941 | Yes | Yes | Yes | No |
| 37.978 | Yes | Yes | Yes | Yes |
TABLE II.
Comparison of algorithms for different data sets
| Method | Set5 PSNR/SSIM | Set14 PSNR/SSIM | BSD100 PSNR/SSIM |
|---|---|---|---|
| Bicubic | 32.40/0.9589 | 31.32/0.9521 | 32.87/0.9563 |
| SRCNN | 33.36/0.9460 | 33.78/0.9366 | 33.57/0.9423 |
| DRCN | 33.57/0.9432 | 33.99/0.9419 | 33.66/0.9410 |
| ESPCN | 34.12/0.9439 | 34.26/0.9412 | 34.23/0.9356 |
| SRGAN | 34.26/0.9356 | 34.89/0.9256 | 34.56/0.9246 |
| Ours | 34.12/0.9326 | 34.56/0.9247 | 34.57/0.9232 |
Figure 9.
Comparison of ppt details
Figure 10.
Comparison of baby details