Table 4.
Comparative computational of different models. For each column, the red indicates the most computationally efficient values for each metric
| Model | #Params (M) | #FLOPs (G) | RT (s) | MOS ↑ |
|---|---|---|---|---|
| UNSB | 14.684 | 253.829 | 9.891 | 26.269 |
| 2D CycleGAN | 11.378 | 631.029 | 0.945 | 40.485 |
| 3D CycleGAN-GL | 47.793 | 1323.729 | 32.140 | 49.746 |
| 3D CycleGAN–2 | 47.793 | 1323.729 | 35.250 | 53.115 |
| 3D CycleGAN–3 | 191.126 | 1585.332 | 94.451 | 56.469 |
Note: ‘#Params’ denotes the total number of trainable parameters in millions (M), ‘#FLOPs’ represents the computational complexity in billions (G) of floating-point operations, and ‘RT’ indicates the average execution time in seconds (s) per image. Lower values in each metric indicate more efficient computational performance. MOS (Mean Opinion Score) rates the subjective quality of images with higher scores reflecting better quality.