声纳图像处理关键技术研究
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摘要
声纳图像处理是海洋研究开发的关键技术,在经济军事等方面都有着重要的研究价值和应用价值,而光学图像处理中的一些算法并不能简单在声纳图像处理中得到有效应用,因此对声纳图像处理的研究是紧迫而有价值的。本文针对声纳图像不同于光学图像的基本特征展开讨论,主要围绕以下几个问题进行研究：问题一：提高声纳图像去噪性能的同时不破坏物体边缘；问题二：获取清晰、可靠、全面的声纳图像；问题三：准确分割声纳图像；问题四：准确实现声纳图像的检索、识别、跟踪等后期处理,并针对这四个问题在四个章节中一一给出相应的解决方案,研究了声纳图像的去噪、融合、分割和检索,通过大量的仿真对比实验验证所提方法的可行性、有效性和可靠性。
对于声纳图像的去噪部分,本文以在光学图像领域中比较流行的小波域图像去噪方法为中心进行探讨。首先,介绍两种较新的小波形式——超小波和形态小波,并将超小波中的轮廓波和表面波、形态小波中的形态哈尔小波和形态中值小波应用在声纳图像去噪中,以探讨其性能是否如在光学图像去噪中有效；其次,在形态小波完备重构条件下,将非线性滤波器中的中点滤波器与形态小波相结合,构建了形态中点小波,并利用多重化、提升、增强等手段对形态中点小波进行强化处理,使其去噪性能更加优异；最后,将各种小波变换应用在声纳图像去噪中进行仿真对比实验,实验数据表明本文所提算法不仅拥有更加优异的去噪性能,而且在保边能力上更加突出。
对于声纳图像的融合部分,本文探讨的是是否可以通过融合的手段使所获声纳图像更加清晰、准确、可靠,仍旧是以光学领域中比较流行的小波域图像融合方法为中心进行探讨。首先,将超小波中的脊波和曲波、以及小波包应用在声纳图像融合中,虽然这些算法在光学图像领域中已经比较成熟,但是声纳图像融合的应用却很少,本文探讨了这些算法在声纳图像融合中的适用情况；其次,源于小波包的思想,本文提出了形态小波包的概念,结合去噪部分构建的形态中点小波,建立了形态中点小波包,将其应用在声纳图像融合中,与现有的其它小波进行仿真对比实验,实验结果表明所提方法融合效果最好；最后,本文将融合技术应用在去噪中,提出了基于多形态小波包基声纳图像融合去噪法,实验结果表明所提方法比单一的形态小波包去噪效果更佳,保边效果更好。
对于声纳图像的分割部分,本文力求寻找一种更加细致全面的分割方法,并能克服在声纳图像分割中阴影带来的负面效应。首先,介绍了光学图像分割中的水平集分割法和谱聚类分割法,将其应用在声纳图像分割系统中,并通过灰度阈值变换预处理克服声纳图像分割中的阴影效应；其次,考虑图像分割与数字抠图之间的联系与区别,现有的图像分割基本上是基于像素点强度或者梯度的运算,而数字抠图考虑的则是透明度或颜色值百分比,因此本文将数字抠图考虑到图像分割中,建立了基于数字抠图的图像分割法,使分割考虑的因素更加全面,并在数字抠图中使用了比较前沿的谱抠图方法,另一方面,针对声纳图像分割时误将阴影作为目标分割出来的弊端,提出了形态学顶帽和底帽变换的预处理方案,建立基于谱抠图的声纳图像分割系统；最后,对各种分割方法进行仿真对比实验,实验数据表明所提方法能够更好的对声纳图像前景目标进行分类。
对于声纳图像的检索部分,本文主要讨论图像检索如何在声纳图像这一特殊图像上实现。首先,介绍了光学图像中占据优势的基于传统互信息的图像检索方法和改进的基于区域生长分割的互信息图像检索方法,并将其应用在声纳图像检索中,探讨其存在的不足；其次,针对声纳图像检索主要基于前景目标检索的具体要求,引入区域互信息的思想,并对前景区域互信息进行加权,将加权区域互信息代替原有的全局互信息,构建基于最大加权区域互信息的声纳图像检索系统；最后,将改进的互信息检索方法与其它互信息检索方法在声纳图像检索系统中进行仿真对比实验,通过仿真对比实验可以看出,所提方法更多的考虑了声纳图像自身的问题,更加适用于声纳图像检索。
Sonar image processing, as the key technology in marine research and development, has important research value and application value in economic and military aspects, and some of the reliable optical image processing algorithms can not simply be effectively applied in sonar image processing, therefore it is urgent and valuable for the research of sonar image processing. Based on the basic characteristics of sonar image different from optical image, this paper did the research focusing on the following several major issues: problemⅠ:to improve the performance of sonar image denoising without destroying the edge; problemⅡ:to obtain clear, reliable, comprehensive sonar image; problemⅢ:to get accurate sonar image segmentation; problemⅣ:to complete sonar image post-processing such as image retrieval, identification, tracking and so on. Then the corresponding solutions for these four questions were given one by one in the four chapters of this paper, sonar image denoising, fusion, segmentation and retrieval were done respectively, and the feasibility, validity and reliability of the proposed methods were verified through a lot of simulation comparing experiments.
For sonar image denoising, this paper discussed the denosing methods in wavelet domain which are more popular in optical image denoising. Firstly, this paper introduced two relatively new wavelet forms-beyond wavelet and morphological wavelet, and made the application of contourlet and surfacelet in the former, morphological Haar wavelet and morphology median wavelet in the latter in sonar image denoising in order to discuss if its performance is as effective as in optical image denoising; secondly, the midpoint filter of nonlinear filter and morphology wavelet were combined together to construct the morphological midpoint wavelet under the perfect reconstruction condition, and then it was improved by multiplying, lifting, and enhancing to get more excellent denoising performance; finally, the simulation comparative experiments were taken and the results showed that the proposed algorithm has not only more excellent denoising performance but also more outstanding edge preserving capacity.
For sonar image fusion, this paper discussed the problem that if getting a clear, accurate and reliable sonar image through fusion technique is feasible, and still in wavelet domain which is more popular in optical image fusion. Firstly, the application of ridgelet and curvelet of beyond wavelet and wavelet packet were maken in sonar image fusion, although these algorithms are relatively mature in optical image processing, they are rarely applied in sonar image fusion, so this paper discussed their feasibility; secondly, this paper proposed the concept of morphological wavelet packet from wavelet packet thoughts, and established morphological midpoint wavelet packet based on morphological midpoint wavelet constructed in denosing part, then applied in sonar image fusion, the simulation comparative experiments results showed that the proposed method is feasible and validity; finally, this paper made the fusion technology applied in image denoising and gave sonar image fusion denoising method based on multiple morphological wavelet packets, the experimental results showed that the proposed method has a better denoising effect and edge preserving ability.
For sonar image segmentation, this paper tried to find a more meticulous and comprehensive segmentation method, and at the same time can overcome the negative effects of the shadow part in sonar image. First of all, several kinds of optical image segmentation methods were introduced:level set method and spectral clustering method, and the shadow effect in sonar image segmentation was overcome through the gray threshold transformation; secondly, by considering the relationship and difference between image segmentation and digital matting:the existing image segmentation is essentially based on pixel intensity or gradient operator, while digital matting considers transparency or the color percentage, this paper combined the two algorithms together to establish a new image segmentation method based on digital matting, and used the forefront spectral matting method in digital matting, on the other hand, aiming at the disadvantage that usually take shadow as target in sonar image segmentation, this paper gave morphological hat and bottom transformation preprocessing plan to set up a sonar image segmentation system based on spectral matting; thirdly, the simulation comparative experiments of various segmentation methods were taken, and the experimental results showed that the proposed method can get a better sonar image forground target segmentation.
For sonar image retrieval, this paper focused on how image retrieval successfully achieved for the special sonar image. Firstly, the existing outstanding image retrieval method based on mutual information and improved region growing segmentation MI in optical image retrieval were introduced, and their shortcomings were explored though the application in sonar image retrieval; secondly, in view of the specific requirement that sonar image retrieval is primarily forground target retrieval, this paper introduced the idea of region mutual information and weighted the forground mutual information, and built the sonar image retrieval system based on maximum weighted region mutual information by means of making the weighted region mutual information instead of the original global mutual information; thirdly, this paper compared the improved mutual information retrieval method with other mutual information retrieval methods in sonar image retrieval system, and the results showed that the proposed method is more suitable for sonar image retrieval.
对于声纳图像的去噪部分,本文以在光学图像领域中比较流行的小波域图像去噪方法为中心进行探讨。首先,介绍两种较新的小波形式——超小波和形态小波,并将超小波中的轮廓波和表面波、形态小波中的形态哈尔小波和形态中值小波应用在声纳图像去噪中,以探讨其性能是否如在光学图像去噪中有效；其次,在形态小波完备重构条件下,将非线性滤波器中的中点滤波器与形态小波相结合,构建了形态中点小波,并利用多重化、提升、增强等手段对形态中点小波进行强化处理,使其去噪性能更加优异；最后,将各种小波变换应用在声纳图像去噪中进行仿真对比实验,实验数据表明本文所提算法不仅拥有更加优异的去噪性能,而且在保边能力上更加突出。
对于声纳图像的融合部分,本文探讨的是是否可以通过融合的手段使所获声纳图像更加清晰、准确、可靠,仍旧是以光学领域中比较流行的小波域图像融合方法为中心进行探讨。首先,将超小波中的脊波和曲波、以及小波包应用在声纳图像融合中,虽然这些算法在光学图像领域中已经比较成熟,但是声纳图像融合的应用却很少,本文探讨了这些算法在声纳图像融合中的适用情况；其次,源于小波包的思想,本文提出了形态小波包的概念,结合去噪部分构建的形态中点小波,建立了形态中点小波包,将其应用在声纳图像融合中,与现有的其它小波进行仿真对比实验,实验结果表明所提方法融合效果最好；最后,本文将融合技术应用在去噪中,提出了基于多形态小波包基声纳图像融合去噪法,实验结果表明所提方法比单一的形态小波包去噪效果更佳,保边效果更好。
对于声纳图像的分割部分,本文力求寻找一种更加细致全面的分割方法,并能克服在声纳图像分割中阴影带来的负面效应。首先,介绍了光学图像分割中的水平集分割法和谱聚类分割法,将其应用在声纳图像分割系统中,并通过灰度阈值变换预处理克服声纳图像分割中的阴影效应；其次,考虑图像分割与数字抠图之间的联系与区别,现有的图像分割基本上是基于像素点强度或者梯度的运算,而数字抠图考虑的则是透明度或颜色值百分比,因此本文将数字抠图考虑到图像分割中,建立了基于数字抠图的图像分割法,使分割考虑的因素更加全面,并在数字抠图中使用了比较前沿的谱抠图方法,另一方面,针对声纳图像分割时误将阴影作为目标分割出来的弊端,提出了形态学顶帽和底帽变换的预处理方案,建立基于谱抠图的声纳图像分割系统；最后,对各种分割方法进行仿真对比实验,实验数据表明所提方法能够更好的对声纳图像前景目标进行分类。
对于声纳图像的检索部分,本文主要讨论图像检索如何在声纳图像这一特殊图像上实现。首先,介绍了光学图像中占据优势的基于传统互信息的图像检索方法和改进的基于区域生长分割的互信息图像检索方法,并将其应用在声纳图像检索中,探讨其存在的不足；其次,针对声纳图像检索主要基于前景目标检索的具体要求,引入区域互信息的思想,并对前景区域互信息进行加权,将加权区域互信息代替原有的全局互信息,构建基于最大加权区域互信息的声纳图像检索系统；最后,将改进的互信息检索方法与其它互信息检索方法在声纳图像检索系统中进行仿真对比实验,通过仿真对比实验可以看出,所提方法更多的考虑了声纳图像自身的问题,更加适用于声纳图像检索。
Sonar image processing, as the key technology in marine research and development, has important research value and application value in economic and military aspects, and some of the reliable optical image processing algorithms can not simply be effectively applied in sonar image processing, therefore it is urgent and valuable for the research of sonar image processing. Based on the basic characteristics of sonar image different from optical image, this paper did the research focusing on the following several major issues: problemⅠ:to improve the performance of sonar image denoising without destroying the edge; problemⅡ:to obtain clear, reliable, comprehensive sonar image; problemⅢ:to get accurate sonar image segmentation; problemⅣ:to complete sonar image post-processing such as image retrieval, identification, tracking and so on. Then the corresponding solutions for these four questions were given one by one in the four chapters of this paper, sonar image denoising, fusion, segmentation and retrieval were done respectively, and the feasibility, validity and reliability of the proposed methods were verified through a lot of simulation comparing experiments.
For sonar image denoising, this paper discussed the denosing methods in wavelet domain which are more popular in optical image denoising. Firstly, this paper introduced two relatively new wavelet forms-beyond wavelet and morphological wavelet, and made the application of contourlet and surfacelet in the former, morphological Haar wavelet and morphology median wavelet in the latter in sonar image denoising in order to discuss if its performance is as effective as in optical image denoising; secondly, the midpoint filter of nonlinear filter and morphology wavelet were combined together to construct the morphological midpoint wavelet under the perfect reconstruction condition, and then it was improved by multiplying, lifting, and enhancing to get more excellent denoising performance; finally, the simulation comparative experiments were taken and the results showed that the proposed algorithm has not only more excellent denoising performance but also more outstanding edge preserving capacity.
For sonar image fusion, this paper discussed the problem that if getting a clear, accurate and reliable sonar image through fusion technique is feasible, and still in wavelet domain which is more popular in optical image fusion. Firstly, the application of ridgelet and curvelet of beyond wavelet and wavelet packet were maken in sonar image fusion, although these algorithms are relatively mature in optical image processing, they are rarely applied in sonar image fusion, so this paper discussed their feasibility; secondly, this paper proposed the concept of morphological wavelet packet from wavelet packet thoughts, and established morphological midpoint wavelet packet based on morphological midpoint wavelet constructed in denosing part, then applied in sonar image fusion, the simulation comparative experiments results showed that the proposed method is feasible and validity; finally, this paper made the fusion technology applied in image denoising and gave sonar image fusion denoising method based on multiple morphological wavelet packets, the experimental results showed that the proposed method has a better denoising effect and edge preserving ability.
For sonar image segmentation, this paper tried to find a more meticulous and comprehensive segmentation method, and at the same time can overcome the negative effects of the shadow part in sonar image. First of all, several kinds of optical image segmentation methods were introduced:level set method and spectral clustering method, and the shadow effect in sonar image segmentation was overcome through the gray threshold transformation; secondly, by considering the relationship and difference between image segmentation and digital matting:the existing image segmentation is essentially based on pixel intensity or gradient operator, while digital matting considers transparency or the color percentage, this paper combined the two algorithms together to establish a new image segmentation method based on digital matting, and used the forefront spectral matting method in digital matting, on the other hand, aiming at the disadvantage that usually take shadow as target in sonar image segmentation, this paper gave morphological hat and bottom transformation preprocessing plan to set up a sonar image segmentation system based on spectral matting; thirdly, the simulation comparative experiments of various segmentation methods were taken, and the experimental results showed that the proposed method can get a better sonar image forground target segmentation.
For sonar image retrieval, this paper focused on how image retrieval successfully achieved for the special sonar image. Firstly, the existing outstanding image retrieval method based on mutual information and improved region growing segmentation MI in optical image retrieval were introduced, and their shortcomings were explored though the application in sonar image retrieval; secondly, in view of the specific requirement that sonar image retrieval is primarily forground target retrieval, this paper introduced the idea of region mutual information and weighted the forground mutual information, and built the sonar image retrieval system based on maximum weighted region mutual information by means of making the weighted region mutual information instead of the original global mutual information; thirdly, this paper compared the improved mutual information retrieval method with other mutual information retrieval methods in sonar image retrieval system, and the results showed that the proposed method is more suitable for sonar image retrieval.
引文
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