Real-time noise reduction for sonar video image using recursive filtering

Cho, Hyeonwoo; Pyo, Juhyun; Gu, Jeonghwe; Jeo, Hangil; Yu, Son-Cheol

doi:10.23919/oceans.2015.7401928

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…59 Table 1 shows the general comparison between underwater optical imaging (underwater optical images) and underwater acoustical imaging (sonar images). 1,2,[5][6][7][8]11,40,59,60 In Table 1, the "Low," "High," and "Bad" are the descriptions compared with the ones of optical imaging (optical images) in the air. The "More low," "More high," and "More bad" are the descriptions compared with the ones of underwater optical imaging (underwater optical images).…”

Section: Brief Description Of Sonar Imagesmentioning

confidence: 99%

“…9 In contrast, acoustical imaging is applicable to long-range detection even though water is turbid. 5 The detection range for acoustical imaging can be thousands of meters even above 10,000 m, for example, 11,000 m for the full ocean depth multibeam system SeaBeam 3012. 10,11 In fact, the acoustical signal is currently the only form of energy radiation that can be transmitted over long range in the ocean.…”

Section: Introductionmentioning

confidence: 99%

“…The methods for sonar image segmentation are researched by the scholars in many countries. There are many sonar image segmentation methods reported such as (1) the ones based on fuzzy c-means (FCM) clustering; [13][14][15][16][17] (2) the ones based on the Markov random field (MRF) model; [18][19][20][21][22][23] (3) the ones based on the snake model and the level set; [24][25][26][27][28][29][30][31][32][33][34][35][36][37] (4) the ones based on spectral clustering; [38][39][40] (5) the ones based on the watershed transform; 41 (6) the ones based on the expectation maximization; 42,43 (7) the ones based on the fractal geometry; [44][45][46] (8) the ones based on deep learning; [47][48][49] (9) the ones based on wavelet (references are discussed in the following); and so on.…”

Section: Introductionmentioning

confidence: 99%

“…2 Optical imaging can obtain the image with better quality than acoustical imaging. [3][4][5] Unfortunately, most ocean waters are rather turbid and optical imaging is not applicable in turbid water. 2,[6][7][8] Deep ocean water (undisturbed) has 6-15 m visibility, while nearshore waters have typically only 1-6 m visibility.…”

Section: Introductionmentioning

confidence: 99%

“…8 Moreover, optical imaging is not applicable to longrange detection. 5 The detection range for optical imaging is below 100 m (using electromagnetic waves like radars). 9 In contrast, acoustical imaging is applicable to long-range detection even though water is turbid.…”

Section: Introductionmentioning

confidence: 99%

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A review on the wavelet methods for sonar image segmentation

Tian

Lan

Guo

2020

International Journal of Advanced Robotic Systems

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The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.

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