# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 3 Results and Discussion
Therefore, the three indexes better meet the conditions of image non-distortion that the mean square error was smaller, the peak signal-to-noise ratio was larger, and the difference of one-dimensional entropy was smaller as well. Thus, the subsequent processes of image registration and image fusion can also be carried out. To prove that the proposed algorithm in this paper can also effectively eliminate many mismatching points on underwater images, Figures 15 and 16 respectively show the image registration effect of two-group images processed by the I-RANSAC algorithm [12] and the IIR algorithm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**\(\boldsymbol{\alpha}\)**} & \multicolumn{2}{c}{**\(\boldsymbol{\beta}\)**} & \multicolumn{2}{c}{**\(\boldsymbol{\gamma}\)**} \\ \cline{2-7}  & **Reference** & **Be-Registered** & **Reference** & **Be-Registered** & **Reference** & **Be-Registered** \\ \hline T\({}_{3}\) & 0.49 & 0.37 & 55.23 & 55.44 & 0.01 & 0.04 \\ T\({}_{4}\) & 0.62 & 0.57 & 57.43 & 57.97 & 0.12 & 0.09 \\ Mean & 0.56 & 0.47 & 56.33 & 56.71 & 0.07 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Three image quality indexes of T3 and T4. Figure 14: The improved images for T4: (**a**) Reference image; (**b**) Be-registered image. Figure 13: The improved images for T3: (**a**) Reference image; (**b**) Be-registered image. As shown in Figures 15 and 16, compared with the I-RANSAC algorithm, the number of lines for matching points in the registration images by the IIR algorithm was reduced, effectively avoiding some mismatching points. To quantitatively prove that the IIR algorithm  
Figure 16: The registered images for T4: (**a**) I-RANSAC; (**b**) IIR. The number of matching pairs of the two algorithms cannot be directly distinguished visually. This is because the number of feature points of the two images in group T4 is too large, and the proportion of false matching pairs is very small. Figure 15: The registered images for T3: (**a**) I-RANSAC; (**b**) IIR. Using IIR algorithm, the number of matching points is less, and many mismatching points are eliminated. can simultaneously improve the accuracy and speed in the process of image registration, Table 6 shows the specific information of the feature points for the two-group images in image registration. Table 7 shows a comparison of the matching accuracy and matching time among the ORB, FAST, SURF, I-RANSAC, and IIR algorithms. Compared with the I-RANSAC algorithm, the IIR algorithm reduced the number of feature points and the number of consistent matching pairs, as shown in Table 6. The reason for this is that the improved algorithm is more rigorous in the selection of feature points and more accurate in the solution of the homology matrix, which effectively eliminates the mismatching pairs. Moreover, according to Table 7, when compared to the I-RANSAC algorithm alone, the IIR algorithm can 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%. Compared to the existing four algorithms (ORB, FAST, SURF, and I-RANSAC), the IIR algorithm is close to the four existing algorithms in matching time but significantly improved the matching accuracy. The average matching accuracy was 92.12%, which was the best among the five algorithms. To further prove that the IIR algorithm can also partly improve the image fusion effect, Figures 17 and 18 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.