# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 3 Results and Discussion  
In this study, image mosaic refers to combining two original images with overlapping regions into one image, which is completed through three steps including image enhancement, image registration, and image fusion. The two original images are respectively defined as the reference image and the be-registered image (the image to be registered). The former can be regarded as a standard reference image without any change, and the latter will be mapped to the former via the spatial transformation with dynamic change. The image processing platform was unified as one laptop, and the detailed information of which was as follows: Windows 10 system, Intel(R) Core(TM) i5-7200u CPU @2.50 GHz 2.71 GHz, and 8 GB of memory. Firstly, two-group images (four images) with low contrast and low definition were randomly selected as experimental objects from the Oxford Buildings Dataset, as shown in Figures 3 and 4. The two-group images are named T1 and T2; and the original images of each group included a reference image and a be-registered image that corresponded to Sub-figure (a) and Sub-figure (b), respectively. Figure 3: The original images for T1: (**a**) Reference image; (**b**) Be-registered image. Reference image is regarded as a standard reference image without any changes, and Be-registered image is mapped to the former through dynamic spatial transformation. The improvement of the image contrast and definition can be visually seen in Figures 5 and 6. Through a quantitative analysis of the data in Table 1, the average mean square error for the reference image and be-registered image were 7.95 and 8.37, respectively; the average peak signal-to-noise ratio for the reference image and be-registered image were 49.42 and 48.86, respectively; and the average one-dimensional image entropy for the reference image and be-registered image were 0.08 and 0.09, respectively. Therefore, the three indexes all 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 also smaller. Thus, there was no artificial distortion between the enhanced images and the original images, and subsequent processes of the image registration and image fusion can be carried out. To prove that the proposed algorithm in this paper can effectively eliminate many mismatching points, Figures 7 and 8 respectively show the image registration effect of two-group images processed by the I-RANSAC algorithm [12] and the IIR algorithm. Figure 6: The improved images for T2: (**a**) Reference image; (**b**) Be-registered image. Figure 7: The registered images for T1: (**a**) I-RANSAC; (**b**) IIR. The blue line indicates the corresponding matching points of the two pictures. The registration accuracy of T1 (**a**) is 89.20%, and that of T1 (**b**) is 91.37%, as shown in Table 3.  
Compared to the I-RANSAC algorithm, in Figures 7 and 8, the number of lines for matching points in the registration images by the IIR algorithm was greatly reduced, effectively avoiding some mismatching points. To quantitatively prove that the IIR algorithm can simultaneously improve the accuracy and speed in the process of image registration, Table 2 shows the specific information of feature points for the two-group images in image registration, and Table 3 shows a comparison of the matching accuracy and matching time among five algorithms, i.e., ORB, FAST, SURF, I-RANSAC, and IIR. 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 2. 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 Homography matrix, which effectively eliminates the mismatching pairs. Moreover, accuracy and rapidity are two important indexes for image registration, which will be displayed as matching accuracy and matching time.