## 2 Proposed Fusion Scheme
Images in Figures 3-8 are the visual outcomes for the balloon, leopard, craft, wine, grass, and calendar images. Table 1 demonstrates the outcomes of the proposed process for a selection of trialed images. Table 2 shows the _RMSE_ and _PSNR_ values, two subjective measurements, for a selection of multi-focus images. In Table 2, the proposed method is compared to methods in the literature that use subjective criteria. The letter 'X' indicates that image specifications for the item are not available. Images of a flowerpot, a clock, a bottle of Pepsi, a cameraman, a desk, a book, a lab, and a flower are used to compare the proposed method to those already in use. The most favourable results are highlighted here. These metrics are practiced to evaluate the resistance of the proposed method to distortion. Conclusions suggest the proposed method fares well in subjective evaluations. ### Comparison of Clock Image Fusion with the Literature

The clock, depicted in Figure 9, is used to evaluate the first multi-focus image. In Figure 9, the original image is depicted in (a). Images with a left focus and a right focus, respectively, are shown in Figure 9b,c. Left-side focus is referred to as a \"left focused image\", while the right-side focus is referred to as a \"right unfocused image\". An image that is right-focused but not left-focused is one that is dominated by the right. The approach is put into practice, and the result is the all-in-focus image, as seen in Figure 9d. The following calculations are used to assess the effectiveness of the proposed methodology: _E_(_F_), _AG_\({}_{F}\), _Q\({}_{E}\), _CC_, _Q\({}^{AB/F}\)_, and _SSIM_. The evaluation of the proposed methodology is then compared to other strategies previously described in the literature. The comparison's findings are displayed in Table 3 of the report. The proposed method outperforms those approaches, according to the literature [20], and the best results of the methods are highlighted. \[TEM_{2}(r,c)=\sum_{m=-1}^{1}\sum_{m=-1}^{1}ASX(r+m,c+n)^{2}matrix_{2}(m+2,n+2) \tag{16}\]1 Multi-focus image of a leopard from various perspectives. Original Image: (**a**), Multi-focus: Input Images: (**b**,**c**), and Proposed Fusion: (**d**). \[TEM_{2}(r,c)=\sum_{m=-1}^{1}\sum_{m=-1}^{1}ASX(r+m,c+n)^{2}matrix_{2}(m+2,n+2) \tag{16}\]2 Multi-focus image of a wine bottle from various perspectives. Original Image: (**a**), Multi-focus Input Images: (**b**,**c**), and Proposed Fusion: (**d**). \[TEM_{2}(r,c)=\sum_{m=-1}^{1}\sum_{m=-1}^{1}ASX(r+m,c+n)^{2}matrix_{2}(m+2,n+2) \tag{16}\]3 Multi-focus image of a balloon from various perspectives. Original Image: (**a**), Multi-focus: Input Images: (**b**,**c**), and Proposed Fusion: (**d**). \[TEM_{2}(r,c)=\sum_{m=-1}^{1}\sum_{m=-1}^{1}ASX(r+m,c+n)^{2}matrix_{2}(m+2,n+2) \tag{16}\]4 Multi-focus image of a _grass_ from various perspectives. Original Image: (**a**), Multi-focus: Input Images: (**b**,**c**), and Proposed Fusion: (**d**). \[TEM_{2}(r,c)=\sum_{m=-1}^{1}\sum_{m=-1}^{1}ASX(r+m,c+n)^{2}matrix_{2}(m+2,n+2) \tag{16}\]5 Multi-focus image of a calendar from various perspectives. Original Image: (**a**), Multi-focus Input Images: (**b**,**c**), and Proposed Fusion: (**d**). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PSNR**} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & \\ \hline Flower & X & X & 4.99 & **76.22** & 7.84 & X & **4.14** & 41.99 \\ Clock & 4.51 & X & 5.85 & 40.5 & X & X & **3.14** & **43.20** \\ Book & **7.04** & X & X & X & X & **3.46** & 38.40 \\ Lab & X & X & X & X & 4.65 & X & **2.69** & 43.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The methodologies from the literature have been compared to a number of images.