Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Huang, Yifan; Li, Weixiang; Yuan, Fei

doi:10.3390/jmse8100761

Cited by 25 publications

(11 citation statements)

References 31 publications

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“…Underwater imaging technology is an important aspect of this work [ 1 ]. However, it is difficult for optical imaging devices to achieve good results owing to the turbidity of seawater and low light levels [ 2 , 3 , 4 ]. In contrast, sonar sensors are well suited to these conditions [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images

Zhao

Chen

et al. 2022

Sensors

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Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model’s ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.

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Section: Introductionmentioning

confidence: 99%

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images

Zhao

Chen

et al. 2022

Sensors

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“…As we all know, the reverberation in a shallow-water waveguide seriously affects the performance of the active array system [1][2][3]. In order to improve the detection ability of the active array, it has important scientific significance to study the mechanism and influence mechanism of seafloor reverberation.…”

Section: Introductionmentioning

confidence: 99%

Horizontal Correlation of Long-Range Bottom Reverberation in Shallow Sloping Seabed

Zhang

Gao

2021

JMSE

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The performance of active sonar detection systems is seriously affected by the reverberation at the bottom of the waveguide in shallow water. In order to improve the performance of active sonar detection, it is necessary to understand the horizontal correlation of shallow-water bottom reverberation in active towed-array processing technology. However, the current research on the spatial correlation of reverberation is mainly based on vertical correlation, little work has been done on the horizontal correlation characteristics of long-distance seabed reverberation, and there is no support from sea test data. In this paper, the coupled mode reverberation model is applied to the horizontal correlation, and is studied according to the receiving position, time, and frequency. The simulation results show that, for the long-range bottom reverberation, the lateral correlation is greater than the longitudinal correlation in the horizontal space. By introducing the adiabatic mode solution, the mathematical model of horizontal correlation in the range-dependent waveguide with depth is derived. The numerical results show that the influence of the seabed dip angle on the horizontal correlation should be considered and that the horizontal correlation is affected obviously by the propagation effects of the sloped sea floor. Finally, the experimental data processing and analysis are given and verify the correctness of the algorithm.

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“…Therefore, the implementation of the existing and the development of the application-specific filtering algorithms for the reconstruction of useful information from the noise-corrupted signals is an important field of scientific research within maritime and transport engineering [8]. The research has been focused on different areas of application, including underwater signal processing [9][10][11][12], radar signal processing [13,14], underwater image processing [15][16][17], optical signal processing [18], and other applications [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

Lopac

Jurdana

Lerga

et al. 2021

JMSE

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The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling more efficient extraction of useful information. This paper proposes a locally-adaptive filtering algorithm based on the radial basis function (RBF) kernel smoother with variable width. The kernel width is calculated using the asymmetrical combined-window relative intersection of confidence intervals (RICI) algorithm, whose parameters are adjusted by applying the particle swarm optimization (PSO) based procedure. The proposed RBF-RICI algorithm’s filtering performances are analyzed on several simulated, synthetic noisy signals, showing its efficiency in noise suppression and filtering error reduction. Moreover, compared to the competing filtering algorithms, the proposed algorithm provides better or competitive filtering performance in most considered test cases. Finally, the proposed algorithm is applied to the noisy measured maritime data, proving to be a possible solution for a successful practical application in data filtering in maritime transport and other sectors.

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Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Cited by 25 publications

References 31 publications

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images

Horizontal Correlation of Long-Range Bottom Reverberation in Shallow Sloping Seabed

Particle-Swarm-Optimization-Enhanced Radial-Basis-Function-Kernel-Based Adaptive Filtering Applied to Maritime Data

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