# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Algorithm Effectiveness Analysis  
After the dual verification of the subjective effects and objective indicators, we confirm that the proposed method is effective. To reiterate, the proposed method involves building two different yet complementary feature extraction modules based on five typical operators, each of which plays a different role. In this section, therefore, we present a detailed analysis  
Figure 5: Fused images using ten different methods when the source image is “FLIR_video_05245”.  
Figure 6: Average values of the six objective indicators for 30 sets of images from the TNO dataset. Here, V1, R, B, D, C, Y, S, A, V2, and O denote VggML, Resnet50, BayF, DDoGAN, CSF, YDTR, SeAFusion, AUIF, VEHMD, and Ours, respectively.  
of the subjective visual maps generated by the mutual promotion of the five operators of the two modules. As shown in Figure 8, we observe that Figure 8(a1,b1) controls the overall contour and Figure 8(c1) reflects that the details are extracted according to scale from small to large and coarse to fine. Their corresponding heatmaps in Figure 8(a2-c2) also represent these feature changes. After the multiplication operation, the captured maps in Figure 8(d1) show integration of each of the feature maps, leading to more uniform image gradient distribution, i.e., the infrared thermal radiation and gradient information is nearly similar to the information distribution of the source images, as verified in Figure 8(d2).  
Similarly, the feature variations of the other module are shown in Figure 9. Unlike the SGCM in Figure 8, which focuses on the spatial gradient features and ignores infrared information, the design of the IBSM in Figure 9 follows two principles: (i) it maintains the overall outline and spatial gradient of the previous module unchanged; and (ii) it extracts infrared information from the source images to the predicted image. Inspired by two metrics related to image exposure, we introduce and improve them in the module to achieve good cross-modal cross-fusion. From Figure 9(a1), the gradient intensity is seen to have clear object outlines and rich infrared brightness, although the visual effect is dim because it is obtained from the horizontal and vertical directions of the source images with the help of the Sobel operator, which has strong edge extraction capability. Subsequently, the exposedness intensity is calculated, and as seen in Figure 9(b1), the brightness features are full. However, maintaining balance between the two intensities when one is too dim and the other too bright is a critical concern. In this case, two weight coefficients \(\omega_{1}\) and \(\omega_{2}\) are used, followed by obtaining supplementary maps. These act similar to a classifier responsible for separating the infrared brightness features from the gradient texture features, as shown in Figure 9(c1,c2). To reduce the noise artifacts, Gaussian smoothing is applied, resulting in refined maps. Finally, the fused images are generated with abundant gradient textures and bright infrared targets.  
Through the above analysis, we demonstrate the motivation for each step of the algorithm. It is also seen from the combination of the subjective visual image and objective indicators that the dual-module complementary strategy is successful.  
Figure 7: Average values of the six objective indicators for 30 sets of images from the RoadScene dataset. Here, V1, R, B, D, C, Y, S, A, V2, and O denote VggML, Resnet50, BayF, DDcGAN, CSF, YDTR, SeA Fusion, AUIF, VEHMD, and Ours, respectively.  
Figure 8: The visual maps and their corresponding heatmaps of SGCM in the fusion framework (Figure 1).  
Figure 9: The visual maps and their corresponding heatmaps of IBSM in the fusion framework (Figure 1).