基于全变分Retinex框架的合成孔径声纳图像均衡方法

doi:10.12382/bgxb.2024.0615

摘要/Abstract

摘要：

针对合成孔径声纳图像灰度不均衡所带来的视觉分辨率低的问题,在结合声纳回波模型、斑点噪声统计特性、分解模型先验假设的基础上,提出一种基于全变分Retinex框架的合成孔径声纳图像均衡方法。根据图像中噪声瑞利乘性分布的特点和极大似然估计推导出考虑背景噪声的合成孔径声纳图像分解模型;针对地形、波束模式、沉积物角度响应和噪声影响,通过对照度分量的分段平滑约束,反射分量的沉积物回波强度信息约束和各向异性全变分约束,建立合成孔径声纳图像分解的目标函数。对照度分量增强后,与反射分量相乘得到均衡图像。实验结果表明,该模型解决了合成孔径声纳图像亮度不均衡的问题,极大地提升了亮度和对比度,在噪声抑制和图像均衡方面优于现有方法。

关键词: 合成孔径声纳, 图像均衡, Retinex全变分, 乘性噪声, 平均距离方向

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

中图分类号:

TN958

钟何平, 雷亮, 李涵, 唐劲松. 基于全变分Retinex框架的合成孔径声纳图像均衡方法[J]. 兵工学报, 2025, 46(8): 240615-.

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-.

图/表 9

图1 复杂地形情况下的海底成像图

Fig.1 Submarine imaging under complex terrain conditions

图2 Retinex变分法图像增强流程

Fig.2 Image enhancement process of Retinex variational method

图3 SAS测试图像

Fig.3 Test images of SAS

图4 不同参数组在6幅测试图像上的平均客观评价指标结果

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

图5 本文算法与对比算法的均衡结果对比图

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

图6 本文算法与对比算法的均衡结果对比图

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

图7 本文算法与对比算法的均衡结果对比图

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

图8 像素-距离向图(图中每条曲线对应图6中的色框)

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

表1 不同均衡算法的客观评价指标比较

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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