## 3 Experimental Results
### Computational Efficiency Evaluation  
To effectively evaluate the computational efficiency of the algorithm, processing time tests were conducted on both the Windows platform and the Linux embedded platform. The Windows platform uses an Intel(r) CoreTM i7-9750H CPU with a clock speed of 2.60 GHz and 8 GB of RAM. The eight algorithms mentioned above were compared with the proposed algorithm. The experimental image resolution was 3840 \(\times\) 2160, and the average processing time for ten multi-focus image fusion tests was recorded. The comparison algorithms were implemented using the source code provided by the authors, with the programming languages being MATLAB and C++.  
In the Windows platform, the average processing time of different algorithms for image fusion is shown in Figure 23. Among them, traditional methods such as CVT, DSIFT, DTCWT, DWT, GF, and NSCT require longer processing time for complex high-resolution images, while the LP and the proposed algorithm in this paper demonstrate superior performance, with fusion times both below 100 ms, enabling real-time fusion. It should be noted that the CNN algorithm is not explicitly included in the figure due to its excessively long inference time.  
The Linux embedded platform has a CPU of HiSilicon SS928V100 (Shenzhen, China), with a clock speed of 1.2 GHz and 1.4 GB of RAM. Since only LP and the proposed algorithm have shorter processing times, and their processing times are in the same order of magnitude in the Windows environment, deployment tests were conducted for LP and the proposed algorithm on the Linux embedded platform. The experimental image resolution was 3840 \(\times\) 2160, and the average processing time across ten repeated trials was recorded. The comparison algorithms were implemented based on the reference and deployed in the same environment and conditions. The programming language used was C++. The average fusion time of LP and the proposed algorithm on the Linux embedded platform is shown in Table 9.  
In the embedded device, the proposed algorithm takes 0.586 s to perform a multi-focus image fusion processing on a 4K image. The processing time of LP is too long and cannot realize real-time processing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **AG** & **STD** & **IE** & **SSIM** & \(Q_{\text{ncle}}\) \\ \hline CVT [27] & 34.8121 & 23.4413 & 6.2375 & 0.8652 & 0.81426 \\ DSIFT [25] & 35.7145 & 25.1411 & 6.2630 & 0.8783 & 0.81697 \\ DTCWT [28] & 33.9993 & 23.229 & 6.2068 & 0.8704 & 0.81488 \\ DWT [30] & 33.8408 & 25.2784 & 6.2818 & 0.8589 & 0.81649 \\ GF [31] & 33.3116 & 24.9922 & 6.2703 & 0.8814 & 0.81469 \\ LP [26] & 35.9339 & 24.5945 & 6.2696 & 0.8771 & 0.81506 \\ NSCT [29] & 34.4149 & 23.4587 & 6.2202 & 0.8779 & 0.81655 \\ CNN [17] & 32.9984 & 23.1295 & 6.2081 & 0.8684 & 0.81651 \\ Proposed & 34.5564 & 25.2728 & 6.2851 & 0.8917 & 0.81902 \\ Proposed * & 34.5718 & 25.2473 & 6.2822 & 0.8940 & 0.82097 \\ \hline \end{tabular}
\end{table}
Table 8: The average values of the evaluation results from Tables 1–6.  
In embedded devices with limited computing resources, LP takes a long time because it requires a large number of floating-point operations to ensure the quality of image fusion. In the proposed algorithm, in the image input stage, the regional interpolation method is used to reduce the image resolution, and in the decision map output stage, the nearest neighbor interpolation method is used to restore the decision map to the original resolution. This effectively reduces the amount of data and improves the computational efficiency of the algorithm. In addition, the pyramid operations used in the focus region detection are all integer operations for 8-bit data, rather than floating-point operations, which is more advantageous on resource-constrained embedded devices and more suitable for deployment in embedded devices