# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Pre-Registered Image Fusion Experiments  
The pre-registered image fusion experiments are designed to assess the image fusion performance of ICSF under pre-registration conditions, primarily assessing the fusion module. We trained the model using the MSRS [3] training dataset, which contains multispectral images from various scenes, helping the model to identify features in visible-infrared image pairs. During the training phase, the MSRS dataset was split into training and validation sets in a 9:1 ratio. For the experimental evaluation, we constructed two distinct test datasets: (1) a subset of 100 randomly selected image pairs from the RoadScene [27] dataset, and (2) the complete set of 25 image pairs from the TNO [28] dataset. This comprehensive test configuration ensures the robustness and generalizability of our experimental results across diverse data sources. These two datasets consist of images of urban road scenes and thermal infrared images that cover many practical application scenarios. The experiment comprehensively evaluates the model's registration and fusion performance in real-world settings using these diverse datasets. Furthermore, the experiment performs a thorough evaluation using quantitative metrics, such as image fusion quality evaluation metrics, and qualitative analysis, such as visual effects. In Figure 7, we present the qualitative comparison results of the proposed algorithm and several mainstream alternatives in pre-registered image fusion. The comparison includes ReCoNet [15], CDDFuse [8], SuperFusion [17], SeAFusion [6], and PSFusion [4]. The proposed method excels in integrating thermal radiation information from the infrared image and detailed texture from the visible image. Thermal radiation objects are distinctly highlighted, allowing for easy differentiation between foreground targets and the background. Furthermore, the fused images exhibit sharp edges and rich contour information by preserving subtle background details often lost in low-light visible images. Table 1 outlines the quantitative results of pre-registered image fusion experiments between the proposed algorithm and five mainstream alternative algorithms on RoadScene and TNO datasets. In this experiment, the entropy (EN) metric was selected to evaluate the information richness of the fused images, while the mutual information (MI) metric was employed to measure the shared information between the fused images and the source images. Additionally, the standard deviation (SD) metric was utilized to quantify the clarity of details in the fused images, and the visual information fidelity (VIF) metric was introduced to assess the perceptual quality of visual information retention from a  
Figure 6: Visualization results of feature filtering and extraction. human perspective. Finally, the \(Q^{ab/f}\) metric was adopted to evaluate the preservation of gradient-domain detail information in the fusion process. The proposed method achieves the best SD, MI, VIF, and \(Q^{ab/f}\) metrics results. Although our method does not show advantages in the EN metric, this is justifiable. The lower entropy value of the fused image in the EN metric is due to a trade-off made by the decoder between model size and image quality. Our method prioritizes retaining image details to ensure that downstream tasks perform effectively. Through weighted evaluation of the test results across two datasets, where the weights were determined by the number of images in each dataset, our proposed method demonstrated a significant improvement in the MI metric, achieving an average enhancement of 30.6% over the second-best method.