Synthetic Aperture Sonar Image Equalization Method Based on the Total Variation Retinex Framework

doi:10.12382/bgxb.2024.0615

Abstract

Abstract:

To address the issue of low visual resolution caused by imbalanced grayscale in synthetic aperture sonar (SAS) images,a synthetic aperture sonar image equalization method based on the total variation framework is proposed by combining the sonar echo model,speckle noise statistical characteristics,and prior assumptions of decomposition model.The synthetic aperture sonar image decomposition model considering background noise is derived based on the Rayleigh distribution characteristics of multiplicative noise in the image and the maximum likelihood estimation.In response to the terrain and beam patterns of illumination component,the sediment angle response of reflection component,and the influence of noise,an objective function for the decomposition of synthetic aperture sonar images is established through the segmented smoothing constraints on the illumination component,the sediment echo intensity information constraints on the reflection component,and the anisotropic total variation constraints.Finally,after the contrast component is enhanced,a balanced image is obtained by multiplying it with the reflection component.The experimental results show that the proposed method solves the problem of uneven brightness of SAS images,greatly improves image brightness and contrast,and outperforms the methods in noise suppression and image equalization.

Key words: synthetic aperture sonar, image equalization, retinex total variation, multiplicative noise, average distance direction

CLC Number:

TN958

ZHONG Heping, LEI Liang, LI Han, TANG Jinsong. Synthetic Aperture Sonar Image Equalization Method Based on the Total Variation Retinex Framework[J]. Acta Armamentarii, 2025, 46(8): 240615-.

Figures/Tables 9

Fig.1 Submarine imaging under complex terrain conditions

Fig.2 Image enhancement process of Retinex variational method

Fig.3 Test images of SAS

Fig.4 Average objective evaluation indexes of different parameter groups on 6 test images

Fig.5 Comparison chart of the equalization results of the proposed algorithm and the comparison algorithm

Fig.6 Comparison chart of the equalization results of the proposed algorithm and the comparison algorithm

Fig.7 Comparison chart of the equalization results of the proposed algorithm and the comparison algorithm

Fig.8 Pixel-distance graph(Each curve in the figure corresponds to the color box in Fig.6)

Table 1 Comparison of objective evaluation indicators of different image equalization algorithms

图序	均衡算法	PSNR	信息熵	标准差	平均梯度
图5	CSRBE算法	11.8200	6.5393	0.2679	0.2261
	改进Retinex算法	8.6010	6.2459	0.2912	0.2679
	Retinex变分算法	13.0645	6.6349	0.2459	0.2912
	本文算法	14.9276	6.6022	0.2206	0.2206
图6	CSRBE算法	10.7884	6.8471	0.2395	0.5206
	改进Retinex算法	8.2343	5.9940	0.2440	0.1853
	Retinex变分算法	13.9184	6.7862	0.2035	0.4518
	本文算法	15.3367	6.6615	0.1934	0.4366
图7	CSRBE算法	11.8277	7.4765	0.2261	0.9387
	改进Retinex算法	6.8141	5.9775	0.1811	0.6497
	Retinex变分算法	13.9590	7.3893	0.1811	0.8040
	本文算法	15.4783	7.2652	0.1601	0.7472

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