## 4 Discussion
### Comparisons of Fusion Results with Different Methods
#### 4.4.1 Qualitative Comparisons  
Figure 11 shows a comparative analysis of fusion results using the \"Queens Road\" image pair. The visible (VIS) image in Figure 11 clearly depicts pedestrians, vehicles, and street lighting, while the corresponding low-brightness image in Figure 11b only retains legible text (\"NERO\") with most other visual information lost. As shown in Figure 11c-j, the existing methods exhibit noticeable limitations, particularly in preserving fine textures and edge details, as evidenced by the blurred or missing features in the marked regions. By contrast, our proposed method (Figure 11k) demonstrates superior performance by simultaneously maintaining thermal targets and enhancing subtle details in low-light areas, as clearly visible in the magnified inset. Figure 12 shows the fusion result of the \"Kaptein_1123\" image pair. Figure 12a,b show the clear image of a passerby. Figure 12c-k display fused images from various methods. CBF, DenseFuse, DRTV, and PM fail to preserve the sky's brightness and introduce artifacts around the people. By contrast, MLGCF, MSID, SeAFusion, RGFTV, and VCAFusion retain the background brightness and key elements. However, VCAFusion excels by preserving edges and details in low-light regions, as seen in the enlarged red area, with clearer details of bushes, ground gaps, and tree branches in Figure 12k. Figure 13 shows the fusion performance analysis for the \"01432\" image pair under challenging visibility conditions. The thermal image (Figure 13a) clearly displays pedestrian thermal signatures, while the corresponding visible (VIS) image (Figure 13b) suffers from significant smoke occlusion that obscures the pedestrian details. Our comparative evaluation reveals that most fusion methods, with the exception of MLGCF, SeAFusion, and our proposed approach, fail to adequately preserve thermal target information. While MLGCF successfully enhances the thermal target brightness, it introduces undesirable  
Figure 11: Fusion results of different methods on the “Queens Road” images. The red boxes highlight the target regions in the magnified local areas. edge blurring in the pedestrian regions. By contrast, both SeAFusion and our method demonstrate robust performances, with our approach exhibiting superior capabilities in (1) maintaining optimal contrast balance, (2) preserving finer texture details (particularly evident in the annotated regions), and (3) achieving more comprehensive information integration from both modalities. Figure 14 presents the fusion results for the \"01454\" image pair, consisting of an infrared (IR) image (Figure 14a) and a visible (VIS) image (Figure 14b) captured under low-light conditions that significantly degrade texture details. A comparative analysis of the nine fusion methods (Figure 14c-k) reveals distinct performance characteristics. DenseFuse and MLGCF exhibit noticeable blurring artifacts in the highlighted region (red box). While DRTV, MSID, SeAFusion, PM, RGFTV, and our method successfully maintain both infrared targets and visible features, our approach demonstrates superior performance in three key aspects, including (1) enhanced contrast optimization, (2) improved detail preservation in architectural elements (window structures), and (3) clearer rendering of natural features (tree branches), as particularly evident in the marked region of Figure 14k. In summary, all nine methods completed the fusion task. MSID and SeAFusion excelled in capturing heat targets and VIS textures but struggled in low-light conditions. Other methods often suffered from brightness imbalances and detail loss. VCAFusion adapted brightness and contrast, avoided artifacts, and retained weak textures and thermal targets effectively, making it versatile in varying lighting environments. Figure 12: Fusion results of different methods on the “Kaptein_1123” images. The red boxes highlight the target regions in the magnified local areas. Figure 13: Fusion results of different methods on the “01432” images.