# Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
## 4 Experiments
### Experiment Results for EDoF Fraunhofer Dataset
Figure 5 provides a visual illustration of the comparison between multi-fused images for all slices within the EDoF Fraunhofer dataset. In cases where the fusion models are designed to accommodate only double-image fusion, generating a fully focused image involved a step-by-step process. It is worth noting that ZMFF cannot obtain effective results as the number of fused images increases. This may be due to the fact that for a series of multi-focus microscopic images, the multi-focus images have a high degree of similarity, which increases the difficulty of zero-shot model optimization. The results produced by the FusionDN, PMGI and U2 Fusion methods exhibit a noticeable and substantial misalignment when compared against the ground truth. With an increase in the number of fused images, the cumulative fused error becomes more pronounced for models designed to support only the fusion of two images. As a result of these pronounced disparities, there appears to be no necessity for a quantitative evaluation of these models. The imagery generated by MFF-GAN and MUFusion prominently showcases synthetic textures that are readily discernible to the observer. Conversely, our proposed U-Swin fusion model has demonstrated exceptional performance, particularly for models utilizing channel mix fusion rules, as emphasized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline  & **MSE** & **PSNR** & **SSIM** \\ \hline FusionDH & \(1129.10\pm 515.22\) & \(18.07\pm 2.03\) & 0.6034 \(\pm\) 0.0115 \\ PMGI & \(5185.17\pm 1817.41\) & \(11.31\pm 1.80\) & 0.5086 \(\pm\) 0.0359 \\ U2 Fusion & \(586.70\pm 204.62\) & \(20.73\pm 1.63\) & 0.6543 \(\pm\) 0.0053 \\ MFF-GAN & \(137.00\pm 24.77\) & \(26.85\pm 0.90\) & 0.9690 \(\pm\) 0.0085 \\ IFCNN (Max) & \(4.99\pm 0.81\) & \(41.21\pm 0.67\) & **0.9844 \(\pm\) 0.0022** \\ IFCNN (Sum) & \(9.03\pm 1.41\) & \(38.62\pm 0.66\) & 0.9779 \(\pm\) 0.0027 \\ IFCNN (Mean) & \(12.31\pm 1.93\) & \(37.28\pm 0.66\) & 0.9768 \(\pm\) 0.0030 \\ Swin Fusion & \(173.41\pm 48.72\) & \(25.96\pm 1.48\) & 0.9785 \(\pm\) 0.0032 \\ MUFusion & \(150.60\pm 44.95\) & \(26.53\pm 1.22\) & 0.8366 \(\pm\) 0.0068 \\ ZMFF (600 Epochs) & \(24.89\pm 7.93\) & \(34.32\pm 1.08\) & 0.9701 \(\pm\) 0.0049 \\ ZMFF (900 Epochs) & \(17.18\pm 5.43\) & \(35.97\pm 1.33\) & 0.9749 \(\pm\) 0.0037 \\ ZMFF (1300 Epochs) & \(14.59\pm 3.54\) & \(36.66\pm 1.34\) & 0.9761 \(\pm\) 0.0033 \\ \hline U-Swin (Max) \(\dagger\) & \(5.36\pm 2.05\) & \(41.17\pm 1.76\) & 0.9829 \(\pm\) 0.0030 \\ U-Swin (Mean) \(\dagger\) & \(15.78\pm 3.60\) & \(36.26\pm 0.96\) & 0.9651 \(\pm\) 0.0070 \\ U-Swin (Simple Mix) \(\dagger\) & \(7.88\pm 2.67\) & \(39.44\pm 1.61\) & 0.9745 \(\pm\) 0.0046 \\ U-Swin (Channel Mix) \(\dagger\) & \(\mathbf{2.74\pm 0.63}\) & \(\mathbf{43.85\pm 0.93}\) & 0.9839 \(\pm\) 0.0020 \\ \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation results for EDoF Fraunhofer dataset for all images. \(\dagger\) means the evaluation results from our proposed model. The values highlighted in bold font represent the best results in their respective evaluation metric columns. \begin{table}
\begin{tabular}{c c c c} \hline  & **MSE** & **PSNR** & **SSIM** \\ \hline MFF-GAN & \(217.62\pm 60.80\) & \(24.95\pm 1.36\) & 0.7340 \(\pm\) 0.0284 \\ IFCNN (Max) & \(7.15\pm 1.38\) & \(39.66\pm 0.80\) & **0.9772 \(\pm\) 0.0030** \\ IFCNN (Mean) & \(12.711\pm 1.93\) & \(37.28\pm 0.66\) & 0.9768 \(\pm\) 0.0030 \\ Swin Fusion & \(208.57\pm 50.52\) & \(25.10\pm 1.28\) & 0.9660 \(\pm\) 0.0051 \\ MUFusion & \(345.51\pm 159.47\) & \(23.17\pm 1.91\) & 0.7405 \(\pm\) 0.0293 \\ \hline U-Swin (Max) \(\dagger\) & \(8.60\pm 3.09\) & \(39.09\pm 1.70\) & 0.9762 \(\pm\) 0.0040 \\ U-Swin (Mean) \(\dagger\) & \(54.10\pm 8.09\) & \(30.84\pm 0.64\) & 0.8911 \(\pm\) 0.0132 \\ U-Swin (Simple Mix) \(\dagger\) & \(8.91\pm 2.73\) & \(38.84\pm 1.41\) & 0.9708 \(\pm\) 0.0053 \\ U-Swin (Channel Mix) \(\dagger\) & \(\mathbf{6.28\pm 1.59}\) & \(\mathbf{40.29\pm 1.07}\) & 0.9649 \(\pm\) 0.0036 \\ \hline \end{tabular}
\end{table}