Denoising on Simulated SM data 2D Lissajous trajectories
41.1PSNRSwinIR
Evaluation results
| Method | Links | ||
|---|---|---|---|
| SwinIRnoise level (σ)=0.062025.11 | 41.1 | 99.3 | |
| RDNnoise level (σ)=0.062025.11 | 40.6 | 99.2 | |
| DnCNNnoise level (σ)=0.062025.11 | 38.9 | 98.9 | |
| SwinIRnoise level (σ)=0.12025.11 | 37.8 | 98.6 | |
| RDNnoise level (σ)=0.12025.11 | 37.5 | 98.6 | |
| DnCNNnoise level (σ)=0.12025.11 | 35.7 | 97.8 | |
| SwinIRnoise level (σ)=0.22025.11 | 33.3 | 96.8 | |
| RDNnoise level (σ)=0.22025.11 | 33.1 | 96.6 | |
| DnCNNnoise level (σ)=0.22025.11 | 31.1 | 94.7 | |
| SwinIRnoise level (σ)=0.32025.11 | 30.6 | 94.6 | |
| RDNnoise level (σ)=0.32025.11 | 30.4 | 94.2 | |
| DnCNNnoise level (σ)=0.32025.11 | 28.1 | 90.4 | |
| DCT-Fnoise level (σ)=0.062025.11 | 25 | 84.1 | |
| DCT-Fnoise level (σ)=0.12025.11 | 21.9 | 76 | |
| DCT-Fnoise level (σ)=0.22025.11 | 18.1 | 57 | |
| DCT-Fnoise level (σ)=0.32025.11 | 16 | 42 |