Table 3 Numeric comparison between different methods on SS-OCT.
From: Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation
| Â | Â | CNR | PSNR | MSR | TP | SSIM |
|---|---|---|---|---|---|---|
SS-OCT Dataset | B2U | 4.341 | 21.853 | 18.631 | 0.998 | 0.523 |
N2S | 4.627 | 22.249 | 15.242 | 0.795 | 0.953 | |
N2N | 5.043 | 23.037 | 27.231 | 0.985 | 0.985 | |
MimicNet | 18.104 | 32.079 | 57.864 | 0.494 | 0.274 | |
BM3D | 20.447 | 35.985 | 75.496 | 0.362 | 0.376 | |
BM4D | 20.429 | 35.87 | 65.965 | 0.348 | 0.327 | |
TGVD | 20.289 | 34.317 | 43.791 | 0.441 | 0.315 | |
GT-SGMM | 8.167 | 24.109 | 6.076 | 0.455 | 0.741 | |
WNNM | 13.245 | 32.581 | 104.7 | 0.490 | 0.661 | |
DARG | 22.239 | 37.212 | 75.971 | 0.422 | 0.194 | |
SDNet | 28.139 | 36.942 | 40.971 | 0.513 | 0.631 | |
Original | 4.622 | 23.993 | 40.676 | 1.0 | 1.0 |
