# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Practical Image Fusion Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & **EN** & **SD** & **MI** & **VIF** & \(Q^{\textit{abf}}\) \\ \cline{3-6}  & & **+** & **+** & **+** & **+** & **+** \\ \hline \multirow{6}{*}{TNO} & ReCoNet & 6.67 \(\pm\) 0.83 & 40.48 \(\pm\) 12.78 & 1.75 \(\pm\) 0.35 & 0.57 \(\pm\) 0.11 & 0.39 \(\pm\) 0.05 \\  & CDDFuse & 7.11 \(\pm\) 0.30 & 45.89 \(\pm\) 12.11 & 2.24 \(\pm\) 0.71 & 0.79 \(\pm\) 0.16 & 0.54 \(\pm\) 0.08 \\ \cline{1-1}  & SuperFusion & 6.80 \(\pm\) 0.38 & 38.95 \(\pm\) 12.40 & 2.5 \(\pm\) 0.46 & 0.72 \(\pm\) 0.11 & 0.5 \(\pm\) 0.06 \\ \cline{1-1}  & SeAFusion & 7.10 \(\pm\) 0.28 & 44.24 \(\pm\) 12.56 & 2.06 \(\pm\) 0.53 & 0.73 \(\pm\) 0.16 & 0.51 \(\pm\) 0.08 \\ \cline{1-1}  & PSFusion & **7.30 \(\pm\) 0.40** & **50.37 \(\pm\) 12.02** & 1.67 \(\pm\) 0.51 & 0.76 \(\pm\) 0.13 & 0.54 \(\pm\) 0.07 \\ \cline{1-1}  & Ours & 7.08 \(\pm\) 0.27 & 45.98 \(\pm\) 13.04 & **3.51 \(\pm\) 0.52** & **0.97 \(\pm\) 0.16** & **0.57 \(\pm\) 0.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & **RMSE** & **MI** & **SCD** & **SSIM** \\ \cline{3-5}  & & \(-\) & \(+\) & \(+\) & \(+\) \\ \hline \multirow{5}{*}{RoadScene} & Superfusion & 60.53 \(\pm\) 50.28 & 2.13 \(\pm\) 0.48 & 1.15 \(\pm\) 0.30 & 0.59 \(\pm\) 0.08 \\  & Ours & 56.92 \(\pm\) 44.46 & 2.26 \(\pm\) 0.42 & 1.22 \(\pm\) 0.26 & 0.59 \(\pm\) 0.08 \\  & ReCoNet \({}^{*}\) & **50.20 \(\pm\) 43.13** & 2.28 \(\pm\) 0.38 & 1.51 \(\pm\) 0.15 & 0.72 \(\pm\) 0.06 \\  & Superfusion \({}^{*}\) & 58.40 \(\pm\) 50.12 & 2.69 \(\pm\) 0.36 & 1.49 \(\pm\) 0.21 & **0.72 \(\pm\) 0.07** \\  & Ours \({}^{*}\) & 60.66 \(\pm\) 46.46 & **3.11 \(\pm\) 0.43** & **1.69 \(\pm\) 0.26** & 0.64 \(\pm\) 0.06 \\ \hline \multirow{5}{*}{FLIRv2} & Superfusion & 53.86 \(\pm\) 38.86 & 2.15 \(\pm\) 0.38 & 1.26 \(\pm\) 0.26 & 0.62 \(\pm\) 0.07 \\  & Ours & 55.15 \(\pm\) 41.47 & 2.06 \(\pm\) 0.42 & 1.10 \(\pm\) 0.42 & 0.62 \(\pm\) 0.07 \\  & ReCoNet \({}^{*}\) & **46.22 \(\pm\) 32.79** & 2.19 \(\pm\) 0.42 & 1.45 \(\pm\) 0.12 & 0.68 \(\pm\) 0.10 \\  & Superfusion \({}^{*}\) & 52.50 \(\pm\) 39.13 & 2.78 \(\pm\) 0.42 & 1.40 \(\pm\) 0.25 & **0.75 \(\pm\) 0.04** \\  & Ours \({}^{*}\) & 51.39 \(\pm\) 40.85 & **3.02 \(\pm\) 0.72** & **1.69 \(\pm\) 0.20** & 0.73 \(\pm\) 0.04 \\ \hline \multirow{5}{*}{IR-DET} & Superfusion & 52.07 \(\pm\) 31.31 & 2.06 \(\pm\) 0.90 & 1.08 \(\pm\) 0.25 & 0.64 \(\pm\) 0.08 \\  & Ours & 53.45 \(\pm\) 33.68 & 1.73 \(\pm\) 0.56 & 1.00 \(\pm\) 0.26 & 0.62 \(\pm\) 0.08 \\  & ReCoNet \({}^{*}\) & **50.11 \(\pm\) 30.36** & 1.95 \(\pm\) 0.54 & 1.29 \(\pm\) 0.26 & 0.59 \(\pm\) 0.15 \\  & Superfusion \({}^{*}\) & 51.66 \(\pm\) 31.35 & 2.50 \(\pm\) 0.55 & 1.27 \(\pm\) 0.24 & **0.68 \(\pm\) 0.07** \\  & Ours \({}^{*}\) & 51.20 \(\pm\) 31.49 & **2.57 \(\pm\) 0.42** & **1.56 \(\pm\) 0.22** & 0.64 \(\pm\) 0.07 \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative results of practical image fusion experiments between the proposed algorithm and two mainstream alternative algorithms on three different datasets. Here, a “\(+\)” indicates that larger metric values are better, while a “\(-\)” denotes the opposite. **Bold** and underlined values represent the best and second-best results, respectively. For each metric, the value preceding “\(\pm\)” denotes the average value of the algorithm on the corresponding dataset, whereas the value following “\(\pm\)” indicates the average error. Asterisked (\({}^{*}\)) methods perform joint registration and fusion; others are fusion only. Figure 8: Qualitative comparison results of practical image fusion experiments between the proposed algorithm and two mainstream alternative algorithms on three different datasets. Asterisked (\({}^{*}\)) methods perform joint registration and fusion; others are fusion only.