# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Group**} & **ORB [6]** & **FAST [4]** & **SURF [5]** & **I-RANSAC [12]** & **IIR** \\  & & **M\(\pm\)SD** & **M\(\pm\)SD** & **M\(\pm\)SD** & **M\(\pm\)SD** & **M\(\pm\)SD** \\ \hline \multirow{3}{*}{Matching} & 5 & \(87.13\pm 1.74\) & \(85.42\pm 2.52\) & \(86.96\pm 1.61\) & \(88.78\pm 1.51\) & **89.34 \(\pm\) 1.37** \\  & 10 & \(88.34\pm 1.81\) & \(88.10\pm 2.44\) & \(88.27\pm 1.75\) & \(89.75\pm 1.28\) & **91.26 \(\pm\) 1.04** \\ \cline{1-1}  & 15 & \(87.92\pm 1.56\) & \(87.68\pm 1.63\) & \(87.71\pm 1.38\) & \(88.96\pm 1.34\) & **91.54 \(\pm\) 1.16** \\ \cline{1-1}  & 20 & \(88.12\pm 1.18\) & \(87.83\pm 1.34\) & \(88.04\pm 0.96\) & \(89.41\pm 0.92\) & **92.17 \(\pm\) 0.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The information of matching accuracy with different groups of images. Figure 10: The fusion images for T2: (**a**) I-RANSAC; (**b**) IIR for T2 (**a**), I-RANSAC registration was used, and the fused images showed stitching traces; for T2 (**b**), IIR registration was used, and the fused images showed no stitching traces. Figure 9: The fusion images for T1: (**a**) I-RANSAC; (**b**) IIR. The image fusions of T1 (**a**) and T1 (**b**) in a simple scene have achieved good results. disappear in the fusion images. The reason for this is that the IIR algorithm can eliminate more mismatching points in image registration, which improves the percentage of accurate matching pairs. Therefore, the fusion effect has also been improved. According to the above experimental results, the following conclusion can be drawn: the proposed IIR algorithm can simultaneously improve the matching accuracy and matching time. Due to the sharp increase of the research cruises, together with the large amounts of images and video recordings for underwater topography, it is a great demand for image mosaic and processing. We have demonstrated the effectiveness of the IIR algorithm in the Oxford Buildings Dataset and then applied it in underwater scenarios. The proposed algorithm has been used to study underwater image mosaic. Two-group images (four images) with low contrast and low definition were randomly selected as experimental objects from the image dataset of manganese nodules, as shown in Figures 11 and 12. Specifically, four images were segmented from the two original images. The purpose of that is to obtain the experimental images with similar sizes and attributes and make the appropriate overlap area between the reference image and be-registered image. Then, two-group images were named T3 and T4, respectively; and the original images of each group similarly included the reference image and be-registered image, which corresponded to Sub-figure (a) and Sub-figure (b). Similar to the process of T1 and T2, Figures 13 and 14 show the enhancement effect of the two-group underwater images. Also, the information of three image quality evaluation indexes corresponding to each group before and after the process of image enhancement is given in Table 5.  
Figure 11: The original images for T3: (**a**) Reference image [21]; (**b**) Be-registered image. Figure 12: The original images for T4: (**a**) Reference image [43]; (**b**) Be-registered image. Compared with Figures 11 and 12, the image contrast and definition in Figures 13 and 14 are significantly improved, which can be intuitively sensed by the naked eye. A quantitative analysis of the data in Table 5 shows that the average mean square error for the reference image and the be-registered image were 0.56 and 0.47, respectively; the average peak signal to noise ratio for the reference image and be-registered image were 56.33 and 56.71, respectively; and the average one-dimensional image entropy for the reference image and the be-registered image both were 0.07.