## 4 Discussion
### Effect Analysis of Free Parameters  
The brightness gain coefficient C in Equation (2) controls the degree of enhancement applied to the source image. A small C value results in insufficient contrast enhancement, failing to reveal object details, while an excessively large C over-amplifies edges, causing contrast imbalance. Figure 8 shows a detailed analysis of histogram evolution under varying enhancement parameters (C = 2 to 10). The results reveal that, while increasing C values effectively expand the histogram and improve image contrast, they introduce characteristic artifacts in the mid-tone range (0.4-0.6 intensity). Specifically, at C = 6, the histogram begins to exhibit subtle discontinuities--manifested as minor gaps and abrupt transitions--due to the algorithm's localized contrast stretching of originally smooth luminance gradients. These artifacts become significantly more pronounced at C = 8, where the histogram shows visible fragmentation and artificial segmentation of continuous tonal distributions. This phenomenon occurs because the enhancement algorithm's aggressive  
Figure 6: Comparison of the proposed method and the other three methods. (**a**) Original low-light image. (**b**) Enhanced using gamma correction. (**c**) Enhanced using GFCE. (**d**) Enhanced using SRIE. (**e**) Enhanced using MSretinex. (**f**) The proposed method. The green and red boxes highlight the target regions in the magnified local areas.  
Figure 7: Fusion results before and after different low-light enhancement methods. (**a**) Visible image. (**b**) Infrared image. (**c**) Fusion result without enhancement. (**d**–**h**) Fusion results using gamma correction, GFCE, SRIE, MSretinex, and our method, respectively. The green and red boxes highlight the target regions in the magnified local areas.  
contrast expansion disproportionately amplifies mid-tone variations, particularly in regions with naturally gradual transitions, like skies or shadows. Importantly, while the artifacts at C = 6 remain below perceptual thresholds and only detectable through statistical analysis, those at C = 8 may become visually apparent in certain scenarios. Our comprehensive evaluation confirms that C = 6 represents the optimal trade-off, providing substantial contrast improvement while maintaining acceptable histogram integrity, whereas higher values prioritize dynamic range expansion at the cost of introducing noticeable artifacts in the mid-tone regions.  
In Equation (4), mean filtering generates the initial brightness gain map but introduces halo artifacts and edge blurring. To mitigate these issues, we employ the SWGF method for refined mapping, which effectively suppresses halos. As illustrated in Figure 9, the optimal results are achieved with parameters \(r\) = 13 and \(\varepsilon\) = 0.01, preserving edge sharpness. Furthermore, Figure 10 analyzes the influence of parameter \(\lambda\) in Equation (17) on fusion performance. At \(\lambda\) = 2, infrared details appear faint. Increasing \(\lambda\) enhances the prominence of infrared features, particularly in bright regions; however, excessively high values (e.g., \(\lambda\) > 20) lead to overexposure. The best fusion performance is attained at \(\lambda\) = 20.  
Figure 8: Variation in the output image and histogram with C.  
Figure 10: Fused results with different parameter \(\lambda\). The red box marks the target area used for comparison.  
Figure 9: Filtering results of different combinations of \(r\), _eps_. The red box marks the target area used for comparison.