# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 3 Results and Discussion
According to Table 3, when compared with the I-RANSAC algorithm alone, the IIR algorithm can greatly improve the matching accuracy and matching time. Regarding the average matching accuracy and average matching time of the two-group images, the IIR algorithm improved the average matching accuracy by 3.54% and reduced the average matching time by 4.30%. When compared with the existing four algorithms of ORB, FAST, SURF, and I-RANSAC, the IIR algorithm was close to the four existing algorithms in matching time but it significantly improved the matching accuracy. The average matching accuracy was 92.12%, which was the best among the five algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Feature Point**} & \multicolumn{2}{c}{**Match Pair**} \\ \cline{3-6}  & & **Reference** & **Be-Registered** & **Rough** & **Uniformly** \\ \hline I-RANSAC & T\({}_{1}\) & 541 & 856 & 213 & 190 \\
[15] & T\({}_{2}\) & 1069 & 1087 & 124 & 109 \\ IIR & T\({}_{1}\) & 478 & 671 & 197 & 180 \\  & T\({}_{2}\) & 716 & 783 & 98 & 91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of feature point information between two algorithms. Figure 8: The registered images for T2: (**a**) I-RANSAC; (**b**) IIR. In complex scenarios, I-RANSAC mismatches, as shown in T2 (**a**), and IIR eliminates mismatches, as shown in T2 (**b**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **Group** & **ORB [6]** & **FAST [4]** & **SURF [5]** & **I-RANSAC [12]** & **IIR** \\ \hline Matching & T1 & 87.76 & 84.71 & 87.12 & 89.20 & **91.37** \\  & T2 & 86.33 & 84.78 & 86.42 & 87.90 & **92.86** \\ accuracy & Mean & 87.04 & 84.74 & 86.77 & 88.55 & **92.12** \\  & T1 & 2.58 & **2.17** & 2.35 & 2.63 & 2.43 \\ Matching & T2 & 2.89 & **2.53** & 2.78 & 2.94 & 2.88 \\ time & Mean & 2.74 & **2.35** & 2.57 & 2.79 & 2.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of the matching accuracy and matching time between different algorithms. Considering the randomness of the two groups of experimental images randomly selected, the experimental results may be unconvincing. Therefore, this paper conducts multiple groups of experiments to verify the effectiveness of the IIR algorithm by obtaining the mean (\(M\)) and standard deviation (\(SD\)). Table 4 shows the specific matching accurate information of the experimental images of different groups. As shown in Table 4, with the increases of the number of groups, in IIR algorithm, the average of matching accuracy takes the first place, while at the same time, the standard deviation of matching accuracy is the smallest, among the five algorithms. So, it is obvious that the IIR algorithm is effective and stable in image registration. To further prove that the IIR algorithm proposed in this paper can also partly improve the image fusion effect, Figures 9 and 10 show the fusion effect of two-group images by the method of weighted smoothing after image registration through the I-RANSAC algorithm [12] and IIR algorithm. As shown in Figures 9 and 10, the same method of weighted smoothing was adopted to image fusion, but the final results were different. Compared with the I-RANSAC algorithm, the image fusion effect by the IIR algorithm is b