Feasibility study of noise reduction for imaging sonar based on time-domain filtering

Abstract

Underwater sonar image can be obtained by the multi-beam imaging sonar. Because of its high frame rate, the imaging sonar can be used for underwater survey especially in turbid water condition. However, the sonar image is generally degraded by the speckle noise. In this paper, we propose a realtime noise reduction algorithm by using the recursive least square algorithm, which is one of the adaptive filters. Through experiments, the performance of the proposed algorithm is analyzed and its feasibility is determined. Also the difference between the proposed algorithm and the conventional noise reduction algorithms that are based on spatial-domain filters are discussed.

A review on the wavelet methods for sonar image segmentation

“…Combing the wavelet transform with a pulse coupled neural network expects to generate precise methods for sonar image segmentation. (7) The wavelet transform has an aptitude for image feature extraction. Deep learning can omnidirectionally percept and memorize the image features so as to precisely classify (or segment) the images if there are a large number of learning samples and sample selection is reasonable.…”

“…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).…”

“…[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. 8 Moreover, optical imaging is not applicable to longrange detection.…”

“…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.…”