Open Access

ARTICLE

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

by Zhenzhou Wang¹, Jiashuo Li¹, Xiang Wang^1,*, Xuanhao Niu²

1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
2 Case Western Reserve University, Cleveland, 44106, USA

* Corresponding Author: Xiang Wang. Email:

Computers, Materials & Continua 2022, 72(2), 4157-4171. https://doi.org/10.32604/cmc.2022.027017

Received 08 January 2022; Accepted 04 March 2022; Issue published 29 March 2022

Abstract

At present, underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system. However, the processed underwater terrain images have inconspicuous and few feature points. In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed, we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block. First, the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information. The accelerated-KAZE (A-KAZE) algorithm is used to combine the feature block information to match the reference image and the target image. Then, the random sample consensus (RANSAC) is applied to optimize the matching results. Finally, image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion. The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information, which not only solves the problem of stitching failure of underwater terrain images due to unobvious features, and further reduces the sensitivity to noise, but also effectively reduces the iterative calculation in the feature point matching process of the traditional method, and improves the stitching speed. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

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Keywords

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