The mosaic of sonar images is more difficult than the mosaic of traditional optical images due to their poor quality and the difficulty in extracting feature points. The existing mosaic methods of sonar images have a series of problems, such as low correct matching rate, large cumulative errors and high requirements for the quality of collected sonar images. In this paper, we proposed a high precision method to implement the mosaic of the underwater sonar video image sequence. Firstly, Accelerated Unsharp Masking (AUSM) algorithm is proposed to preprocess the original image. Then we extract KAZE feature points from preprocessed sonar images. A matching method combining multiple matching strategies with Progressive Sample Consensus (PROSAC) algorithm is followed to complete the image registration. Weighted fusion method and a region of interest (ROI) acquisition method based on the slope of right border is used to optimize the mosaicked image. Finally, we can obtain a high-quality panoramic image of underwater sonar video by a global mosaic strategy. Mosaic experiments on the sonar video image sequence collected by multi-beam sonar demonstrate that the proposed method in this paper can increase the number of feature points by about 70% and make the correct matching rate higher than 70%. The proposed method also has good robustness and the cumulative error during multi-image mosaic is less.

由于声纳图像质量差且难以提取特征点，因此声纳图像的镶嵌比传统光学图像的镶嵌更困难。现有的声纳图像拼接方法存在正确匹配率低，累积误差大，对声纳图像质量要求高等一系列问题。本文提出了一种高精度的水下声纳视频图像序列拼接方法。首先，提出了加速模糊锐化掩模（AUSM）算法对原始图像进行预处理。然后，我们从预处理的声纳图像中提取KAZE特征点。遵循将多种匹配策略与渐进样本共识（PROSAC）算法相结合的匹配方法来完成图像配准。基于右边界的斜率的加权融合方法和感兴趣区域（ROI）获取方法被用于优化镶嵌图像。最后，我们可以通过全局镶嵌策略获得水下声纳视频的高质量全景图像。对多束声纳采集的声纳视频图像序列进行马赛克实验表明，该方法可以将特征点的数量增加约70％，正确匹配率高于70％。所提出的方法还具有良好的鲁棒性，并且在多图像拼接期间的累积误差较小。对多束声纳采集的声纳视频图像序列进行马赛克实验表明，该方法可以将特征点的数量增加约70％，正确匹配率高于70％。所提出的方法还具有良好的鲁棒性，并且在多图像拼接期间的累积误差较小。对多束声纳采集的声纳视频图像序列进行马赛克实验表明，该方法可以将特征点的数量增加约70％，正确匹配率高于70％。所提出的方法还具有良好的鲁棒性，并且在多图像拼接期间的累积误差较小。