A Mosaic Method for Side-Scan Sonar Strip Images Based on Curvelet Transform and Resolution Constraints

Abstract

Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.

A High–Efficiency Side–Scan Sonar Simulator for High–Speed Seabed Mapping

“…These images reflect the topographic features and acoustic information in the scanning area. Then, the upper computer combines the navigation information to form a complete seabed mapping image [ 18 , 19 ].…”

A Curvelet-Transform-Based Image Fusion Method Incorporating Side-Scan Sonar Image Features

“…The last or utmost layer, called the fine layer, epitomizes the intricate details within the high-frequency spectrum. In this layer, the dimensions of the coefficient matrix correspond precisely to the image size [17], as presented in Table 1.…”

“…The transform domain image fusion technique enables analysis followed by a conversion of side-scan sonar images from the spatial domain to other domains. For instance, image fusion based on the wavelet transform [6,[8][9][10] decomposes wavelets of adjacent strip images into multilevel frequency domain coefficients and fuses these coefficients, which are then inversely transformed into the new side-scan sonar mosaic image. An examination of images from the perspective of the frequency domain suggests that the method mitigates the limitations of spatial domain analysis in capturing critical information.…”

“…For instance, the Canny operator can extract edge contours from the high-frequency components obtained upon wavelet decomposition, prioritizing edge information during fusion and recombination [12]. The wavelet transform can be blended with weighted averaging to process wavelet coefficients [8,13,14]. Imagery can be divided into high-frequency and low-frequency components for processing using pyramid methods and wavelet transform, respectively [15].…”

“…While overcoming the limitations of the wavelet transform, the curvelet transform also addresses the inadequacy of edge detection operators under multiscale analysis [16]. Despite that, current side-scan sonar image fusion techniques expanding on the curvelet transform still deliver suboptimal fusion outcomes as they fail to take full account of the features of side-scan sonar imagery [17].…”