Table 7. Fused images evaluation metrics of different algorithms.
Where the best results are highlighted in bold.
| Fusion method | Evaluation metrics | ||||||
|---|---|---|---|---|---|---|---|
| QAB/F | QMI | PSNR | SSIM | SF | STD | ||
| Spatial domain | GFF (Li, Kang & Hu, 2013) | 0.5978 | 3.127 | 42.9419 | 0.4865 | 31.7488 | 63.882 |
| MSA (Du et al., 2016a) | 0.3038 | 2.8495 | 28.75 | 0.4829 | 17.0158 | 55.9475 | |
| Transform domain | NSCT + LE (Zhu et al., 2019) | 0.5184 | 2.6052 | 26.0083 | 0.4861 | 31.337 | 75.5464 |
| NSST + PAPCNN (Yin et al., 2018) | 0.5206 | 2.6297 | 25.2629 | 0.4914 | 31.7002 | 77.6748 | |
| Deep Learning | Proposed Algorithm NSCT + CNN − VGG19 | 0.5415 | 3.8606 | 44.8445 | 0.6509 | 31.66083 | 77.2692 |