Image Super Resolution On Ixi
Metriken
PSNR 2x T2w
PSNR 4x T2w
SSIM 4x T2w
SSIM for 2x T2w
Ergebnisse
Leistungsergebnisse verschiedener Modelle bei diesem Benchmark
| Titel der Arbeit | Quellcode | |||||
|---|---|---|---|---|---|---|
| EDSR+MMHCA | 40.43 | 32.70 | 0.9469 | 0.9877 | Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution | |
| SERAN | 40.30 | 32.62 | 0.9472 | 0.9874 | MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network | |
| CSN | 39.71 | 32.05 | 0.9413 | 0.9863 | Channel Splitting Network for Single MR Image Super-Resolution | |
| IDN | 39.09 | 31.37 | 0.9312 | 0.9846 | Fast and Accurate Single Image Super-Resolution via Information Distillation Network | |
| RDN | 38.75 | 31.45 | 0.9324 | 0.9838 | Residual Dense Network for Image Super-Resolution | |
| CNN-IL | 38.67 | 30.57 | 0.9210 | 0.9837 | Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans | |
| VDSR | 38.65 | 30.79 | 0.9240 | 0.9836 | Accurate Image Super-Resolution Using Very Deep Convolutional Networks | |
| SRCNN | 37.32 | 29.69 | 0.9052 | 0.9796 | Image Super-Resolution Using Deep Convolutional Networks | |
| T2Net | 29.38 | 28.66 | 0.8500 | 0.8720 | Task Transformer Network for Joint MRI Reconstruction and Super-Resolution |
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