# Underwater Image Mosaic Algorithm Based on Improved Image Registration
###### Abstract  
In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance. image mosaic; IIR; image enhancement; SIFT; weighted smoothing 2021 10.3930/app1113986  
17 May 2021  
Accepted: 18 June 2021  
Published: 27 June 2021  
**Published: Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Published:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. scale-invariance. Accuracy, rapidity, and stability are three important evaluation indexes in the study of image registration [7]. In terms of the purification of matching point pairs, [PERSON] et al. [8] eliminated some mismatching points by modifying the threshold values of Euclidean distance and integral images, and realized the effective utilization of feature points, but the registration accuracy was still not high. [PERSON] et al. [9] proposed an improved SIFT algorithm based on the idea of block matching, which had a good registration effect for remote sensing images. To solve the problem of algorithm stability, [PERSON] et al. [10] proposed an iterative SIFT algorithm based on adaptive non-maximum suppression, which realized the high robustness of UAV cognitive navigation but also increased its complexity. Aimed at the problem of poor registration effect caused by simply improving SIFT, scholars started to combine SIFT and the random sample consensus (RANSAC) algorithm for image registration, which was famous for its robustness. Even if there are a few wrong data points, it can still obtain an ideal model parameter. [PERSON] et al. [11] presented a comprehensive overview of existing research in RANSAC-based robust estimation, and provided a universal framework for RANSAC (USAC) algorithm. [PERSON] et al. [12] presented an improved RANSAC (I-RANSAC) algorithm for SIFT feature point matching. [PERSON] et al. [13] proposed an algorithm based on a double-column histogram, which has a good application in remote sensing images. [PERSON] et al. [14] proposed an algorithm based on the consistency principle of matching distance and realized high matching accuracy. [PERSON] et al. [15] proposed a dynamic distributed robust RANSAC algorithm, which improved the stability index. [PERSON] et al. [16] proposed a rotating average pipeline based on layered RANSAC to handle relative rotational outliers, and improve the accuracy index. [PERSON] et al. [17] proposed an image mosaic algorithm based on effective blur technology, which has good recognition performance for motion blur images. In terms of image fusion, classical fusion algorithms include direct fusion, weighted smooth fusion, PCA fusion, wavelet transformation fusion, and multi-scale transformation fusion [18]. The traditional RANSAC method has obvious disadvantages, because it only produces reasonable results under certain probability, and improves the accuracy through multiple iterations of the optimal solution of model parameters, which increases the complexity of the algorithm.