# Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
## 4 Experiments
### Experiment Results for EDoF Fraunhofer Dataset  
Many end-to-end fusion models are limited to the fusion of only two images, which poses a challenge when dealing with multi-focus microscope images that consist of a series of z-slices. To ensure a fair evaluation of fusion model performance, we selected two microscope images from the EDoF Fraunhofer dataset. Since ZMFF is a zero-shot image fusion model and the model should be trained for each fusion step, we evaluated the results for different training epochs (600 epochs, 900 epochs and 1300 epochs). The comparative fusion examples generated by various models are presented in Figure 4. Significantly, the fused image generated by Fusion DN exhibits conspicuous errors along its edges, indicating a notable shortfall in edge fidelity. Furthermore, the fused images resulting from PMGI and U2 Fusion display a discernible reduction in luminance, while PMGI manifests pronounced and undesirable shadow artifacts. In contrast, the images produced by MFF-GAN, Swin Fusion and MUFusion exhibit higher luminance when compared to both the ground truth generated from the complex wavelet extended-depth-of-field method and the source input images. Surprisingly, the ZMFF model achieved satisfactory performance for multi-focus microscope image fusion after 500 epochs of training without priors learned from large-scale datasets. Notably, IFCNN and U-Swin, each employing distinct fusion rules, excel in generating fusion images that are universally regarded as superior in terms of quality. This subjective assessment underscores their prominence in achieving the highest levels of image fusion quality. Table 1 presents the quantitative evaluation results for the EDoF Fraunhofer dataset. The values highlighted in bold font represent the best results in their respective evaluation metric columns. These values represent the mean and standard deviation of the evaluation metrics across the entire dataset. The statistics evaluation results listed in Table 1 clearly indicate that our proposed U-Swin fusion model, particularly when employing channel mix fusion, achieves the best performance in terms of mean squared error (_MSE_) and ranks second in structural similarity (_SSIM_). Notably, the fused images from MFF-GAN and Swin fusion exhibit relatively lower peak signal-to-noise ratios (_PSNR_s), which correlates with comparable performance in _SSIM_. In contrast, MUFusion cannot achieve optimal performance for multi-focus microscope image fusion. Surprisingly, the ZMFF model achieved comparable performance to U-Swin (mean) and IFCNN (mean) without prior training from large-scale datasets. However, Since ZMFF needs to be retrained every time image fusion is performed, this will take a lot of time. It is worth noting that the other fusion models, with the exception of IFCNN and ZMFF, struggle to generate satisfactory fusion images. This difficulty is often attributed to the characteristics of microscope images, which typically exhibit low contrast and repetitive structures that pose challenges for feature extraction. The majority of fusion models, with the notable exceptions of IFCNN and the U-Swin fusion model, conduct the fusion process within the Y component of the YCrCb color space. This approach, while effective for many types of images, may not be optimally suited for microscope images. The Y component primarily encodes the brightness information, which is important for overall image quality but may not adequately capture the nuances of microscope images. As a result, these models tend to yield less favorable results when assessed using metrics like mean squared error (_MSE_) and peak signal-to-noise ratio (_PSNR_). The limitations arise from the fact that microscope images often exhibit unique characteristics such as low contrast, repetitive structures and lower signal-to-noise ratios, which demand more sophisticated fusion techniques for accurate and high-quality results. Figure 4: The comparison example of two selected multi-focus fused images from EDoF Fraunhofer dataset.