Speckle Noise Filtering in Side-Scan Sonar Images Based on the Tucker Tensor Decomposition

Real signals are usually contaminated with various types of noise. This phenomenon has a negative impact on the operation of systems that rely on signals processing. In this paper, we propose a tensor-based method for speckle noise reduction in the side-scan sonar images. The method is based on the Tucker decomposition with automatically determined ranks of factoring tensors. As verified experimentally, the proposed method shows very good results, outperforming other types of speckle-noise filters.

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Integrating ensemble empirical mode decomposition and nonlinear anisotropic diffusion filter for speckle noise reduction in underwater sonar images

10.1117/12.876729 ◽

2011 ◽

Cited By ~ 1

Author(s):

Somayeh Bakhtiari ◽

Sos Agaian ◽

Mohammad Jamshidi

Keyword(s):

Noise Reduction ◽

Empirical Mode Decomposition ◽

Anisotropic Diffusion ◽

Speckle Noise ◽

Ensemble Empirical Mode Decomposition ◽

Diffusion Filter ◽

Anisotropic Diffusion Filter ◽

Mode Decomposition ◽

Sonar Images ◽

Speckle Noise Reduction

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Reduction of Multiplicative Noise in Radar Images

Journal of the Russian Universities Radioelectronics ◽

10.32603/1993-8985-2021-24-4-6-18 ◽

2021 ◽

Vol 24 (4) ◽

pp. 6-18

Author(s):

A. A. Tuzova ◽

V. A. Pavlov ◽

A. A. Belov

Keyword(s):

Noise Reduction ◽

Similarity Index ◽

Human Perception ◽

Speckle Noise ◽

Radar Image ◽

Optimal Parameters ◽

Radar Images ◽

Speckle Noise Reduction ◽

Noise Filters

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

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Learning a Deep Convolutional Network for Speckle Noise Reduction in Underwater Sonar Images

Proceedings of the 2019 11th International Conference on Machine Learning and Computing - ICMLC '19 ◽

10.1145/3318299.3318358 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yuxu Lu ◽

Ryan Wen Liu ◽

Fenge Chen ◽

Liang Xie

Keyword(s):

Noise Reduction ◽

Speckle Noise ◽

Convolutional Network ◽

Sonar Images ◽

Deep Convolutional Network ◽

Speckle Noise Reduction

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Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Journal of Marine Science and Engineering ◽

10.3390/jmse8100761 ◽

2020 ◽

Vol 8 (10) ◽

pp. 761

Author(s):

Yifan Huang ◽

Weixiang Li ◽

Fei Yuan

Keyword(s):

Deep Learning ◽

Noise Reduction ◽

Dictionary Learning ◽

Acoustic Waves ◽

Autonomous Underwater Vehicles ◽

Speckle Noise ◽

Learning Method ◽

Sonar Image ◽

Sonar Images ◽

Speckle Noise Reduction

As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved.

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