## 3 Results and Discussion
### Experimental Results  
Three pairs of morphology and phase images of Staphylococcus aureus, called \"Data-1\",\" Data-2\",\" Data-3\", respectively, are selected as the representative results for subjective evaluation. In the results of using LP, CVT, and NSST-VGG, although the internal structure of the cell is very clear, the cell boundary is very blurred, and the surrounding phase image noise is mixed together. All the results using FusionGAN have clear boundary information, but in \"Data-1\" and \"Data-3\", the results using FusionGAN have a lack of internal structure information, as shown in Figures 5g and 6g. And in Figure 7g, the result shows only the boundaries of the internal structure. This shows that the FusionGAN fusion effect is not stable. In \"Data 3\", the contrast of GFT results is low, but it performs better in \"Data-1\" and \"Data-2\". This means that GFT has similar problems with FusionGAN. The results of the proposed method could clearly show the internal structures and contours of the cells, and the intracellular structures and cytoplasmic regions are with high contrast. But in terms of visual sense, its performance is not as good as the better results in GFT and FusionGAN. Figure 5: Fusion results of the “Data-1” image set with different methods. (**a**) Morphology; (**b**) Phase; (**c**) Laplacian pyramid (LP); (**d**) curvelet transform (CVT); (**e**) Non-Subsampled Shearlet Transform (NSST)-VGG; (**f**) gradient transfer fusion (GFT); (**g**) FusionGAN; (**h**) proposed. Figure 6: Fusion results of the “Data-2” image set with different methods. (**a**) Morphology; (**b**) phase; (**c**) LP; (**d**) CVT; (**e**) NSST-VGG; (**f**) GFT; (**g**) FusionGAN; (**h**) proposed. Table 1 showed the quantitative comparisons of the four metrics for six methods, and shows the \(p\)-values of the proposed method for different metrics of each comparison method. It was demonstrated that the proposed method is the best in almost all metrics except GFT, and the t-test showed that there is a significance difference to the other methods. Although GFT has the highest score on \(Q^{LLSIM}\), the \(p\)-values in Table 2 showed that there is no significant difference from the proposed method. The quantitative analysis results are consistent with the subjective visual comparison results. LP, CVT, and NSST-VGG are all scale fusion methods, the performance of the three methods is relatively close, and there is a large gap with the proposed method. The GFT method performs better when the morphology image and the phase image have large gray levels, such as \"data-1\", but when the two are close, the fusion image will be blurred. \(Q^{AB/F}\) indicates the fusion effects of edge information, GFT results are the worst. FusionGAN requires a lot of data for training, and AFAM cell images cannot meet this requirement. The network comes from an infrared image fusion project [21] with similar targets, which cannot achieve good results for AFAM cell image fusion, and the result is unstable. The FusionGAN method is very difficult to train and not easy to converge. It is difficult to achieve a good fusion effect even with a large number of images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(\mathbf{MI}\) & \(\mathbf{Q}^{\text{AB/F}}\) & \(\mathbf{Q}^{\text{LLSIM}}\) & **VIFF** \\ \hline LP & 0.5614 & 0.6279 & 0.9309 & 1.2307 \\ CVT & 0.5614 & 0.4309 & 0.9390 & 0.9317 \\ NSST-VGG & 0.5707 & 0.5438 & 0.9376 & 1.1296 \\ GFT & 0.5978 & 0.4444 & **0.9663** & 0.8473 \\ Fusion & & & & \\ GAN & 0.6771 & 0.2319 & 0.9458 & 0.6397 \\ Proposed & **0.6827** & **0.6532** & 0.9551 & **1.3062** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean values of Quality metric for different methods with 16 cell images.