## 3 Experimental Results
### Quantitative Evaluation
#### 3.2.2 Objective Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **AG** & **STD** & **IE** & **SSIM** & \(\mathbf{Q_{\text{n}}}\)**cie** \\ \hline CVT [27] & 12.3632 & 10.7028 & 5.54002 & 0.909865 & 0.81881 \\ DSIFT [25] & 12.4924 & 12.0779 & 5.54811 & 0.919278 & 0.823326 \\ DTCWT [28] & 11.5306 & 10.6387 & 5.49342 & 0.912781 & 0.819688 \\ DWT [30] & 11.9218 & 12.0684 & 5.5686 & 0.921628 & 0.823993 \\ GF [31] & 11.9296 & 11.6672 & 5.52452 & 0.922296 & 0.822911 \\ LP [26] & 13.3136 & 11.2697 & 5.58323 & 0.912336 & 0.820555 \\ NSCT [29] & 12.4523 & 10.6912 & 5.55654 & 0.910408 & 0.819554 \\ CNN [17] & 10.4320 & 10.3358 & 5.5252 & 0.91216 & 0.82354 \\ Proposed & 12.951 & 11.9026 & 5.64674 & 0.924828 & 0.824449 \\ Proposed * & 12.936 & 11.8209 & 5.68476 & 0.9263 Through quantitative analysis of six different sample groups (ten-year pine stem, Capsella older embryo, housefly wing, circuit board, battery holder circuit, and sensor circuit), the proposed algorithm demonstrates superior performance in most SSIM and \(Q_{\text{ncle}}\) metrics, with particularly notable advantages in electronic component samples (e.g., circuit board SSIM reaches 0.929614, sensor circuit \(Q_{\text{ncle}}\) reaches 0.828196). Compared to traditional methods, DSIFT preserves details well but is sensitive to noise; frequency-domain approaches such as CVT, DTCWT, and NST tend to suffer from high-frequency feature loss; CNNs often produce over-smoothed results in biological samples, though performance improves with electronic samples. Table 8 summarizes the average metric values of each algorithm across the six samples. The results show that the proposed method achieves the highest scores in IE (6.2851), SSIM (0.8940), and \(Q_{\text{ncle}}\) (0.82097), and ranks second only to DWT in STD (25.2473 vs. 25.2784). This indicates that the proposed algorithm achieves an effective balance between structural fidelity and detail preservation, demonstrating excellent performance in both biological tissue and electronic component inspection, making it particularly suitable for industrial detection scenarios requiring micron-level precision. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **AG** & **STD** & **IE** & **SSIM** & \(Q_{\text{ncle}}\) \\ \hline CVT [27] & 53.0906 & 28.6474 & 6.80511 & 0.858056 & 0.819864 \\ DSIFT [25] & 53.5911 & 28.2358 & 6.77732 & 0.866079 & 0.81903 \\ DTCWT [28] & 52.4223 & 28.441 & 6.79821 & 0.862685 & 0.820285 \\ DWT [30] & 51.3696 & 28.1972 & 6.77436 & 0.870141 & 0.82007 \\ GF [31] & 51.4012 & 28.1351 & 6.77095 & 0.872878 & 0.820718 \\ LP [26] & 53.109 & 29.375 & 6.83198 & 0.867835 & 0.819624 \\ NST [29] & 53.008 & 29.0032 & 6.81995 & 0.865614 & 0.82048 \\ CNN [17] & 49.8133 & 28.8426 & 6.75652 & 0.863738 & 0.812352 \\ Proposed & 49.4289 & 28.8073 & 6.84009 & 0.866481 & 0.821801 \\ Proposed \({}^{*}\) & 49.4128 & 28.7056 & 6.85168 & 0.867186 & 0.821704 \\ \hline \end{tabular}
\end{table} 
Table 6: The evaluation results of the fused images shown in Figure 21 for the battery holder circuit sample. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **AG** & **STD** & **IE** & **SSIM** & \(Q_{\text{ncle}}\) \\ \hline CVT [27] & 32.5517 & 16.5784 & 5.95006 & 0.91023 & 0.815478 \\ DSIFT [25] & 33.1308 & 17.065 & 5.93774 & 0.89124 & 0.820368 \\ DTCWT [28] & 31.6427 & 16.2941 & 5.93283 & 0.917782 & 0.816438 \\ DWT [30] & 30.3168 & 16.7354 & 6.02287 & 0.762649 & 0.815396 \\ GF [31] & 26.2456 & 17.1092 & 6.06482 & 0.874978 & 0.80518 \\ LP [26] & 33.7763 & 17.6949 & 5.99203 & 0.921932 & 0.816358 \\ NST [29] & 32.7835 & 16.8062 & 5.94666 & 0.92872 & 0.816188 \\ CNN [17] & 31.6019 & 16.8886 & 6.00345 & 0.818782 & 0.818782 \\ Proposed & 32.0278 & 17.7999 & 5.88154 & 0.924163 & 0.827311 \\ Proposed \({}^{*}\) & 32.0652 & 17.8013 & 5.87165 & 0.925105 & 0.828196 \\ \hline \end{tabular}
\end{table} 
Table 7: The evaluation results of the fused images shown in Figure 22 for the sensor circuit sample.