Edge preserving filter of side scan sonar images with wavelet modulus maxima shift‐correlative technique

Abstract

An edge preserving filter algorithm of side scan sonar (SSS) image based on wavelet modulus maxima shift-correlative (WMMS) technique is proposed in this article. First, the proposed WMMS algorithm decomposes SSS image into multiscale wavelet coefficients. Then the modulus maxima, which are produced by catastrophe points, are extracted from wavelet coefficients. The algorithm matches these maxima across the different scales to identify signal or noise. After correcting the ''drifting'' phenomenon of modulus maxima, a correlation factor array of wavelet coefficients is constructed by strengthening the maxima dominated by signal and suppressing those maxima dominated by noise. By correlating wavelet coefficients with the correlation factor array, the WMMS strengthens the useful high-frequency signal and weakens the noise. Finally, the algorithm restores SSS image from revised wavelet coefficients. We apply the WMMS algorithm to filter SSS images of the experimental sea areas. Results show that WMMS has advantages over traditional algorithms in suppressing noise and preserving useful high-frequency information.

Combined imaging matching method of side scan sonar images with prior position knowledge

“…The method deconstructs the SSS image into multi-scale time-frequency domains and identifies the signal and noise components by matching the maxima across the different scales. By correlating wavelet coefficients, the highfrequency signal is strengthened, and the noise is weakened [20].…”

Inversion of Side Scan Sonar Motion and Posture in Seabed Geomorphology

“…In this context, it is of practical significance to study inversion and compensation of side scan sonar motion. Although data redundancy of side scan sonar is not high, there is a common coverage area in its strip image data, and regional pattern varies with the changes in motion and posture [12]. So the platform motion, posture change parameters can be calculated according to morphological parameters of the common coverage area.…”

Mosaic method of side‐scan sonar strip images using corresponding features

“…Through decoding the observation file and transforming the sound intensity into grayscale, the waterfall image of seabed relief, namely an echo sequential image in the order of recording time along the track line, is formed. This image does not have the information on location or measurability, so the data preprocessing, such as geometric correction and geocoding of each pixel, is needed [16][17][18][19][20]. The preprocessing procedure of raw SSS strip image is shown in Fig.…”