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.
Highlights
With the development of sonar imaging technology, the underwater sonar image detection technology has been extensively used in marine exploration, research, and investigation [1]
Because of the complexity of the underwater environment, the underwater sonar image is affected by the reverberation effect, strong speckle noise, fuzzy edge, and weak texture information [4]
The underwater sonar image is represented as X = {x1, x2 · · ·, xn}, n is the total number of pixels. xi is the ith pixel. xi is the non-local spatial information of xi and can be expressed as: xi = ∑ wipxp p∈Wir where Wir represents the search window, its centre is xi and the radius is r. wip and zi can be calculated by: wip
Summary
With the development of sonar imaging technology, the underwater sonar image detection technology has been extensively used in marine exploration, research, and investigation [1]. A promising and multiphase detection framework related to the level set was developed [13], the key idea of which was to minimize the energy with the local mean This framework was not suitable for the detection of underwater sonar images in the presence of speckle noise. An adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) was proposed to solve the problem of the underwater sonar image detection [22], which accurately completes underwater object detection by searching clustering centres. The improvement of the detection algorithm focuses on two aspects: Firstly, inspired by the idea of data field and entropy [23], the AIA-DF is adopted to more accurately calculate the initialization of the clustering centres in this paper to enhance convergence efficiency. In the belief space, to improve the limitations of the update strategy, a new update strategy is used to update the cultural individuals in terms of QSFLA
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