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Overview: An understanding of the ocean and its changing environment is increasingly important. Scientific, economic, and political decision-making depends to some extent on this knowledge. However, even lasers can penetrate through only a few tens of meters in very clear water. Acoustic waves, by contrast, can travel over long distances without much attenuation. Therefore, all kinds of sonars play an important role in ocean research. Side-scan sonar (SSS) is an electronic device that utilizes the propagation characteristics of sound waves under water to complete underwater detection and communication tasks through electro-acoustic conversion and information processing. Because the SSS produces images and maps according to the intensity of acoustic echo, speckle noise will inevitably be involved due to the complex underwater environments. Block-matching and 3D filtering (BM3D) is an advanced denoising method based on the fact that an image has a locally sparse representation in transform domain. This sparsity is enhanced by grouping similar 2D image patches into 3D groups. This algorithm performs well in dealing images polluted by Gaussian additive noise. The BM3D algorithm was originally designed for Gaussian additive noise, therefore, it is not reasonable to denoise the side-scan sonar images polluted by speckle noise. In this paper, a speckle denoising method based on BM3D is proposed to filter the multiplicative speckle noise in side-scan sonar images. First, the SSS image is transformed by power and logarithm. The multi-scale two-dimensional discrete wavelet transform is used to estimate the general noisy level of the polluted image. Second, the parameters of the BM3D algorithm are updated according to the noise estimation results of each local patch. Third, after comparing the general noise estimation and the local noise estimation, the proposed algorithm chooses the best estimation to filter every patch separately to solve the problem that the noise is not evenly distributed. Finally, the image properties are recovered by exponential transformation and inverse power transformation. The experimental results show that the improved BM3D algorithm can effectively reduce the speckle noise in SSS images and obtain good visual effects. In this paper, three non-reference image quality evaluation parameters, namely the equivalent noise of looks (ENL), speckle reduction index (SSI), speckle suppression and average preservation index (SMPI), are used to evaluate the noise reduction effect. Compared with two kinds of improved BM3D algorithms and a traditional algorithm, the ENL of the proposed algorithm is at least 6.83% higher than that of others, its SSI is very similar to that of Manhattan distance-based adaptive block-matching and 3D filtering(MD-ABM3D), and its SMPI is reduced by at least 3.30%. This method is mainly used for sonar image noise reduction, and has certain practical values for ultrasonic, radar or OCT images polluted by speckle noise.
The proposed algorithm
Original SSS images for test. (a) Boat image; (b) Undersea bulge image; (c) Undulating seabed topography image
Sonar image processed by algorithm. (a)~(c) Original image; (d)~(f) Euclid distance; (g)~(i) Manhattan distance
Real side scan sonar image algorithm processing result. (a), (g), (m) Median filtering; (b), (h), (n) NLM; (c), (i), (o) Original BM3D algorithm; (d), (j), (p) Fan et al.[18]; (e), (k), (q) MD-ABM3D; (f), (l), (r) Proposed algorithm