An Adaptive Denoising and Detection Approach for Underwater Sonar Image

Abstract

An adaptive approach is proposed to denoise and detect the underwater sonar image in this paper. Firstly, to improve the denoising performance of non-local spatial information in the underwater sonar image, an adaptive non-local spatial information denoising method based on the golden ratio is proposed. Then, a new adaptive cultural algorithm (NACA) is proposed to accurately and quickly complete the underwater sonar image detection in this paper. Concretely, NACA has two improvements. In the first place, to obtain better initial clustering centres, an adaptive initialization algorithm based on data field (AIA-DF) is proposed in this paper. Secondly, in the belief space of NACA, a new update strategy is adopted to update cultural individuals in terms of the quantum-inspired shuffled frog leaping algorithm (QSFLA). The experimental results show that the proposed denoising method in this paper can effectively remove relatively large and small filtering degree parameters and improve the denoising performance to some extent. Compared with other comparison algorithms, the proposed NACA can converge to the global optimal solution within small epochs and accurately complete the object detection, having better effectiveness and adaptability.

Evaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network

“…In this paper, the non-local spatial information denoising algorithm [25] based on the golden ratio is used to remove the noise points in underwater sonar images. On this basis, the effectiveness and adaptability of DB-PCNN are verified.…”

“…The NACA [25] was proposed in our previous work. Compared with our proposed ACA-IQPSO [27] and QSFLA-NSM [28], the NACA has good initialisation results and can complete underwater object segmentation faster and more accurately.…”

Radar and Sonar Imaging and Processing

“…Interesting research on the processing of side-scan sonar images aiming at detection of targets is presented by Wang et al [17]. Taking into account the fact that the denoising and detecting of underwater sonar images is crucial for the proper interpretation of the image, the authors proposed a new adaptive approach for this.…”

Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers

“…However, SFLA can quickly fall into local minima and has slow convergence in the later stage of the evolution and poor calculation accuracy (Elbeltagi et al, 2007), and thus it is commonly coupled with other advanced algorithms to find global optimal solutions effectively (Orouji et al, 2013;Ahandani andAlavi-Rad, 2015). One of the crucial topics concentrates on the quantum-inspired SFLA (QSFLA) characterized by certain principles of quantum mechanisms for a typical computer, and QSFLA was successfully used for its fast convergence (Gao andCao, 2012;Wang et al, 2019). Since there is rarely any published work to deal with many-objective optimization problems using QSFLA based on the Pareto theory, we use a real-coded quantum computer and an external archive with dynamic updating mechanism to save and update the non-dominated solutions in the multiobjective r-QSFLA (r-MQSFLA).…”