## 3 Experimental Results
### Quantitative Evaluation
#### 3.2.1 Subjective Visual Comparison  
The multi-focus image dataset was processed using DSIFT, DWT, GF, CVT, DTCWT, LP, NSCT, CNN, and the proposed algorithm in this paper. The fusion experimental results of the multi-focus image dataset shown in Figures 6-11 are presented in Figure 17, Figure 18, Figure 19, Figure 20, Figure 21, and Figure 22, respectively. Figure 19: Fusion images of houselfly wing w.m. dataset shown in Figure 8 under different algorithms. Figure 20: Fusion images of the circuit board sample dataset shown in Figure 9 under different algorithms. Figure 21: Fusion images of the battery holder circuit sample dataset shown in Figure 10 under different algorithms. Figure 17 shows the fusion images of the Ten Year Pine Stem C.s. dataset shown in Figure 6 under different algorithms. Based on the comparison results of the nine algorithms, it can be concluded that DSIFT, DWT, GF, and the proposed method perform relatively well in the fusion of microscopic images of pine stems, effectively preserving the structure of cell walls and the details of pits. Among these, the proposed algorithm demonstrates the best overall performance--it not only clearly presents the continuous texture of the cell wall but also fully retains the key morphological features of the pits, achieving an optimal balance between detail enhancement and structural preservation. In contrast, CVT, DTCWT, LP, NST, and CNN exhibit significant deficiencies: CVT introduces blocky, mosaic-like artifacts in the intercellular regions; DTCWT causes wave-like distortions along the cell walls; LP severely blurs the cell textures; NST introduces granular noise in the cytoplasmic areas; and CNN results in locally abnormal cell expansion due to overfitting. Although the GF algorithm performs well overall, it still generates artifacts along the cell boundaries. Traditional multi-scale methods and deep learning approaches continue to face challenges in handling complex textures in biological tissues--NST tends to over-enhance regions with uneven staining, while CNN struggles to adapt to the multi-scale geometric characteristics of cell walls. Figure 18 shows the fusion images of the Capsella older embryo Sec. dataset shown in Figure 7 under different algorithms. DSIFT, DWT, GF, CNN, LP, and the proposed method demonstrate relatively effective fusion outcomes, preserving key cellular textures within the red-boxed regions. Notably, the proposed method achieves optimal clarity in vascular bundle structures and stomatal complexes while avoiding artifacts prevalent in other methods: CNN and LP exhibit edge blurring in palisade mesophyll cells, DSIFT introduces grid-like distortions in intercellular spaces, and GF shows faint halo effects around trichome bases. CVT, DTWCT, and NST underperform significantly, failing to resolve critical details--CVT produces checkerboard artifacts in spongy mesophyll, DTWCT distorts chloroplast distribution patterns, and NST introduces speckle noise in epidermal cell junctions. Figure 19 shows the fusion images of the housefly wing w.m. dataset shown in Figure 8 under different algorithms. DSIFT, DWT, GF, and the proposed algorithm perform well in overall clarity, effectively fusing the sharp regions of the wing. CVT, DTCWT, and NST exhibit poor overall sharpness, with noticeable blurring and artifacts. CNN has an advantage in local contrast but may suffer from over-smoothing or artificial traces. When examining  
Figure 22: Fusion images of the sensor circuit sample dataset shown in Figure 11 under different algorithms. the zoomed-in details of the wing veins, DSIFT and the proposed algorithm demonstrate superior performance, clearly preserving fine textures without obvious artifacts. Figure 20 shows the fusion images of the circuit board sample dataset shown in Figure 9 under different algorithms. DSIFT and the proposed algorithm demonstrate superior performance in the circuit board fusion results, delivering sharp pin edges, clear solder joints, and well-preserved fine textures with minimal artifacts.