# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Ablation Study  
Ablation experiments were conducted to verify the effectiveness of different modules and loss functions. To ensure fair comparison in the ablation study, all modules were maintained with the same number of training epochs as described in Section 5.1. In this study, the RoadScene dataset was selected and partitioned into training and test sets at a ratio of 9:1 to conduct ablation experiments. To quantitatively evaluate the image fusion performance, the mutual information (MI) metric was adopted, while both mutual information (MI) and root mean squared error (RMSE) metrics were utilized to validate the registration performance. Additionally, the computational cost (GFLOPs) and the number of parameters (\(N_{p}\)) were employed to effectively balance the algorithm's performance and resource consumption. Table 5 shows the experimental results. When the encoder's convolutional dictionary was replaced with a single \(7\times 7\) single-layer convolution, the results showed that using ADB as the convolutional dictionary significantly improved the quality of the fused image. When the feature filters were replaced with a CNN block composed of four concatenated convolutional layers, the results revealed a significant increase in both computational cost and the number of parameters. However, the quality of the fused images was disappointing, indicating that the choice of algorithm for the feature filtering network is crucial for effective feature extraction and reducing information loss. The experimental results showed a decline in fusion image quality when the dynamic gated convolution block in the fusion module was removed; introducing a dynamic gated convolution block improves fusion quality. When the input to the registration module (RM) was changed from unregistered homologous feature pairs \(\{f_{h}^{x},f_{h}^{y}\}\) to traditional unregistered image pairs \(\{T^{x},I^{y}\}\), keeping the network parameters remained the same; the RMSE increased, signifying insufficient registration. This increase demonstrates that using unregistered homologous feature pairs as input improves the registration process efficiency. Modifications to the loss function did not affect the computational cost (GFLOPs) or parameter count (\(N_{p}\)), and thus, these values remain consistent with those of ICSF in subsequent experiments. When the feature-guided loss was modified to \(\frac{\big{(}CCC(T_{h}^{x},I_{h}^{y})\big{)}^{2}}{0.01+CCC(T_{h}^{x},I_{h}^{y})}\), the results showed that, under the same parameters, the modified loss function produced poorer fusion image quality than the original feature-guided loss. Therefore, the proposed feature-guided loss function is more efficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **MI** & **GFLOPs** & \(N_{p}(\mathbf{10^{6}})\) & **FPS** \\ \cline{2-5}  & **+** & **—** & **—** & **+** \\ \hline ReCoNet & 2.19 \(\pm\) 0.42 & 12.54 & 209 & 2.23 \\ CDDFuse & 2.41 \(\pm\) 0.69 & 19.42 & 1186 & 1.45 \\ SuperFusion & 2.15 \(\pm\) 0.38 & 2.26 & 138 & 12.49 \\ SeAFusion & 2.36 \(\pm\) 0.52 & 2.72 & 167 & 10.51 \\ PSFusion & 1.86 \(\pm\) 0.58 & 9.62 & 45,895 & 3.02 \\ Ours & 3.57 \(\pm\) 0.66 & 2.69 & 136 & 10.43 \\ \hline ReCoNet * & 2.19 \(\pm\) 0.42 & 12.54 & 209 & 2.25 \\ Superfusion * & 2.78 \(\pm\) 0.42 & 4.09 & 1962 & 7.00 \\ Ours * & 3.02 \(\pm\) 0.72 & 3.53 & 417 & 8.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed algorithm and mainstream alternative algorithms in single fusion and joint fusion and registration experiments.The FPS (frames per second) represents the frame rate achieved when deploying and processing the algorithms on a Raspberry Pi 5. Here, a “+” indicates that larger metric values are better, while a “\(-\)” denotes the opposite. Asterisked (*) methods perform joint registration and fusion; others are fusion only.