# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Practical Image Fusion Experiments  
The primary objective of the practical image fusion experiments is to evaluate the performance of the registration and fusion model when handling unregistered and deformed images in real-world scenarios. This experiment is specifically structured to assess the performance of the registration and prediction modules. We used the deformed MSRS as the training dataset. Unlike standard datasets, the deformed dataset introduces factors such as geometric transformations and illumination changes, making it more representative of real-world conditions. During the training phase, the MSRS dataset was split into training and validation sets in a 9:1 ratio. During the validation phase, we meticulously constructed three distinct test datasets to evaluate our method's performance under various conditions comprehensively: (1) a subset of 100 randomly selected image pairs from the deformed version of the RoadScene dataset, (2) a subset of 100 randomly chosen image pairs from the unregistered FLIRv2 [29] dataset, and (3) a subset of 140 randomly selected image pairs from the unregistered IR_DET [30] dataset. This multi-faceted validation strategy was designed to rigorously assess the robustness and adaptability of our approach across different deformation states and registration scenarios. In these test sets, the images contained varying levels of registration errors and were captured under different real-world conditions. Figure 8 compares the results of the proposed algorithm and several mainstream alternatives in practical image fusion. The comparison includes ReCoNet and Superfusion. Most texture details are accurately registered in road scenes. Even in images where significant RGB details are missing, the proposed method, which does not rely on strict registration, still delivers relatively better registration and fusion quality. Table 2 details qualitative comparison results of practical image fusion experiments between the proposed algorithm and two mainstream alternative algorithms on three different datasets. In this experiment, the root mean squared error (RMSE) metric and the structural similarity index (SSIM) metric were selected to ensure geometric consistency and comprehensive consistency in luminance, contrast, and structural information between the registered images and the reference images, respectively. Furthermore, the mutual information (MI) metric was employed to evaluate the global statistical similarity between the registered images, while the structural consistency difference (SCD) metric was utilized to assess structural misalignment in the registered images. The proposed algorithm excels in the MI and SCD metrics, achieving the highest scores, and also ranks second in the SSIM metric for three of the test sets. Through weighted evaluation of the test results across three 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 8.3% over the second-best method.