Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

Abstract

The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

“…The side scan sonar image correction strategy based on the transform domain decomposes the image into low-frequency and high-frequency sub-bands for feature analysis. This mainly includes the Laplace pyramid transform (LP) [11], wavelet transform (WT) [19], curvelet transform (curvelet) [20], contourlet [21], and non-subsampled contourlet transform (NSCT) [22]. Among them, side scan sonar images' high-dimensional variation information cannot be reflected by the LP and WT approaches.…”

Side-Scan Sonar Image Segmentation Based on Multi-Channel CNN for AUV Navigation

“…The segmentation result is established in a data set that has been accurately marked, which reduces the robustness of the model. Zhou et al ( 2020 ) proposed a sum-modified Laplacian energy filtering with a CNN model for image fusion of side-scan sonar. This paper proposes a sum-modified Laplacian energy filtering and improved dual-channel pulse coupled neural network for image fusion of side-scan sonar in non-subsampled contourlet transform.…”

A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

“…With the gradual deepening of research on single-and dual-channel PCNN, the method is becoming one of the most popular method in the image fusion. Compared with the wavelet transform [1] that has been applied to multi-focus image fusion, FSD [2] and Gradient pyramid [3], PCNN is still a research focus of multi-focus image fusion, medical image fusion, and the well-known works are [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…Then, a weighted average fusion rule using the coefficient of variation according to the different imaging characteristics of SAR images in the low-frequency sub-band of the decomposition image, the effect of SAR image noise on the fused image is removed by setting the coherence coefficient threshold in the high-frequency sub-band of the decomposition image. Because the calculations in prior research are complex, model in [15] has been further improved. Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network is obtained, An improved PCNN model is proposed in this paper .…”