基于脉冲耦合神经网络的图像处理若干问题研究
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摘要
脉冲耦合神经网络(Pulse Coupled Neural Networks, PCNN)是基于生物视觉系统机理形成的具有模数混合处理、串并联混合处理及动态自适应处理的一种空时编码新型人工神经网络。由于PCNN模型的动态变阈值、非线性调制耦合、同步脉冲发放、动态脉冲发放及时空总和等特性,使其在图像处理、自动目标识别、组合优化、人工生命等领域的研究和应用得到国内外的广泛重视。
本文围绕图像处理中脉冲噪声滤除、高斯噪声滤除、弱小目标检测、二值图像自动分割、多值图像自动分割及基于内容的图像检索等若干关键问题,针对脉冲耦合神经元结构复杂性及其在图像信号处理中存在阈值反复衰减、自适应性能差和无法自动选择最佳处理结果等一些不足,研究了PCNN模型的机理,并提出改进思路与方法。论文主要内容如下:
1.为有效滤除图像中严重脉冲噪声的干扰,提出了基于脉冲耦合神经网络噪声检测的两级脉冲噪声滤波算法。首先在改进自适应单位连接PCNN(AULPCNN)模型的基础上,利用其同步脉冲发放特性区分定位脉冲噪声点和信号像素点位置,其次根据噪声点局部邻域信息对噪声点进行第1级自适应滤波,然后再对前一级的滤波输出利用具有保护边缘细节特点的多方向信息中值第2级细微辅助滤波。该算法在噪声检测中无需设定检测阈值,噪声检测精度较高;在去噪过程中不但有效滤除噪声干扰,而且能很好地保护图像边缘细节等信息,具有较好的主观视觉效果和客观评价指标,去噪能力强、信噪比高和适应性好,特别是对受严重噪声污染的图像,显示了更大的优越性。
2.针对图像高斯噪声的去除,提出了一种基于改进型脉冲耦合神经网络的双边滤波算法。在考虑图像高斯噪声特征的前提下,引入平滑抑制因子和自适应链接强度,并与相似神经元同步激活特性相结合,形成平滑抑制自适应连接PCNN(SIAL-PCNN)模型,然后应用在含噪图像预滤波迭代处理中,在滤除极值噪声的同时形成反映图像空时信息的赋时矩阵,最后将生成的赋时矩阵信息运用在双边滤波中,并对其进行了自适应性改进与滤除高斯噪声的处理。该算法在较好保护图像边缘细节等信息的情况下,能有效地滤除平滑区域噪声,在信噪比和去噪能力方面都有一定的提高。
3.从含单一弱小目标图像特征出发,提出了结合灰度熵变换的脉冲耦合神经网络小目标图像检测算法。该方法在对含随机噪声和有复杂背景的图像进行非线性灰度熵变换滤波的基础上,考虑灰度熵值映射图在满足目标背景比先验概率的条件下,利用局部最小交叉熵判据,自动选取包含单一小目标局部窗口作为处理图像区域,并进行改进型PCNN迭代检测处理。该算法能自动可靠地检测出复杂背景及随机噪声干扰下的弱小目标。
4.为自动对图像进行二值分割,提出了一种新的自适应迭代全局阈值图像自动分割算法。首先对二维超模糊集隶属函数进行自适应修正,并将其引入到图像超模糊熵概念中,然后从适应图像分割角度考虑,将传统脉冲耦合神经网络模型改进为具有单调指数上升阈值函数的单位链接脉冲耦合神经网络(ULPCNN)抑制捕获模型,最后把ULPCNN与最大超模糊熵判据相结合对图像进行自动分割。该算法能自动确定最佳分割阈值,对图像目标划分清晰、细节保持较好,改善了图像的分割性能。
5.考虑原始图像与分割图像之间的相互关系,以最大互信息为分割目标,以互信息熵差作为一种新的分类判据,在对传统脉冲耦合神经网络模型改进的基础上,提出了一种基于最大互信息改进型PCNN多值图像自动分割算法。该算法能够自动确定最佳分割迭代次数及最佳分割灰度类数,对分割图像具有良好的特征划分能力,且在分割类数较少的情况下,能较好地保持图像细节、纹理及边缘等信息,对图像分割精度高,具有较强的适用性。
6.为简单有效地提取图像重要特征信息,从而更好地提高检索图像的精度,提出了一种基于脉冲耦合神经网络的图像归一化转动惯量(NMI)特征提取及检索算法。首先利用改进简化PCNN模型相似神经元同步时空特性及指数衰降机制,将图像分解为一系列具有相关性的二值图像,然后提取能反映原图像目标形状、结构分布的系列二值图像的一维NMI特征矢量信号,并将其应用在图像检索中。同时,考虑到系列二值图像间的相关性及不同图像间NMI序列值的差异性,引入了马氏距离结合Pearson积矩相关法的综合相似性度量方法。所提算法对图像特征矢量序列具有良好抗几何畸变不变特性及对图像表述的唯一性,且有较好的图像检索效果。
Pulse Coupled Neural Networks (PCNN) , which is provided with analog-digital mixed processing, series-parallel mixed processing and dynamic adaptive processing technologies,is a novel space-time coding artificial neural network developed on basis of the mechanism of biological visual system. Since the characteristics of dynamic threshold, nonlinear modulation coupling, synchronous pulse bursts, dynamic pulse bursts and space-time summation, PCNN is emphasized in numerous fields such as image processing, automatic target recognition, combinatorial optimization, and artificial life at home and abroad.
This paper discusses several key problems of image process, such as impulse noise and Gaussian noise filtering, small target detection, automatic segmentation of binary images, automatic segmentation of multi-threshold images and content-based image retrieval. According to the complex structure of pulse coupled neurons and its defects in image process, such as repeat attenuation of threshold, low adaptive capacity and lack of the automatic selection ability of optimal processing result, this paper studies the model principle of PCNN, proposes improved method, and makes some progresses as follows:
1. In order to effectively remove high dense impulse noise in images, a novel two-stage impulse noise filtering algorithm based on improved PCNN noise detection is put forward. Firstly, on the basis of improved adaptive unit-linking PCNN (AULPCNN), the noisy points and signal points are distinguished and located using the synchronous pulse burst property of AULPCNN, and then the noisy points are smoothed by the first adaptive filter based on their local adjacent information of the noisy points. Secondly, the assistant filtering using the second multi-direction information median filter, which can protect edges details, is adopted. The proposed algorithm needs no detection threshold, which has higher accuracy in the noise detection, the impulse noise can be effectively removed, and edges and details can be preserved well. Meanwhile, the de-noise images obtained by the proposed algorithm have better objective quality and subjective vision effect than other filtering algorithm. This algorithm presents higher signal to noise ratio, stronger capability to reduce noise and better adaptive, and more advantage especially to the high noisy polluted image.
2. Aim at the removal of image Gaussian noise in images, bilateral filtering algorithm based on improved PCNN is put forward. Firstly, on the basis of the characteristics of Gaussian noise in images, the smooth inhibitory factor and adaptive linking strength is introduced, combined with the synchronized activation of similar neurons, and then the adaptive linking PCNN with smooth inhibitory factor (SIAL-PCNN) model is formed. Secondly, the noisy image is processed by using SIAL-PCNN pre-de-noise iteration. The extreme value noise is removed and the time matrix that can reflect image spatial-temporal information is also generated. Finally, the time matrix is operated in bilateral filtering which is given adaptive improvement and de-noise application. The proposed algorithm can effectively remove noise in smooth region in the case of good preserving image edges and details. Meanwhile, its signal to noise ratio and the capability to reduce noises are better increased.
3. A new method based on PCNN and gray scale entropy,is proposed for image segmentation and detection,starting from the aspect of characteristics of single small target image.Based on nonlinear gray scale entropy transform on an image with complex background and stochastic noise,this algorithm takes into account the condition that the mapping images of gray scale entropy satisfy the prior probability of the object to background ratio,and select the local region including a single small target which can be regarded as image processing part.Iterative detection using improved PCNN is utilized under the criterion of local minimum cross-entropy.The novel method can detect small target with the disturbance of complex background and random noise reliably.
4.In order to process the binary segmentation of an image automatically, a new adaptive iterative image segmentation algorithm with the property of global threshold is proposed. Firstly, the membership function is adaptively modified and introduced into the concept of image ultra-fuzzy entropy on the basis of analysis and discussion of two-dimension ultra-fuzzy sets theory. Secondly, the traditional PCNN model is improved into the restraining capture PCNN model with the monotony exponential raised threshold function from the point of view of image segmentation. Finally, the improved ULPCNN is combined with the criterion of maximum ultra-fuzzy entropy, which the best image segmentation is processed. The proposed algorithm automatically determines the number of iterative times, chooses the best threshold, separates the objects in the image clearly, preserves most of the details, and enhances the performance of image segmentation.
5. The traditional PCNN model is improved firstly, a new algorithm of multi-threshold image segmentation using improved PCNN based on the maximization of mutual information is put forward according to the relationship between original image and segmented image, which is based on the object of maximal of mutual information and a new measurement criterion for determining the clusters of an image called difference of mutual information. The new algorithm can automatically determine the optimal cyclic iterative times and the optimal number of gray-scale clusters, has a favorable capability to carve up characteristics and maintain the edges, texture and details of images, has higher precision in image segmentation and can be more adaptability.
6.In order to simply and effectively extract the information of important features in the image so as to improve the accuracy of the image retrieval, a novel algorithm of image normalized moment of inertia (NMI) feature extraction and retrieval based on PCNN is put forward. Firstly, the image is segmented into a series of binary correlation images using synchronous spatial-temporal characteristics of similar neurons and exponential attenuation mechanism of improved and simplified PCNN, and then a one-dimensional NMI feature vector signal of the binary series images, which can reflect the target shape and structure of the original image, is extracted, and applied to the image retrieval. Meanwhile, considering the correlation of binary series images and NMI sequence values differences between different images, the method of compounded similarity measurement of the combination of Mahalanobis distance and Pearson product-moment correlation is introduced. The proposed algorithm has good performance of anti-geometric distortions and the uniqueness for different images expression to the vector sequence of image features, and has better image retrieval results.
本文围绕图像处理中脉冲噪声滤除、高斯噪声滤除、弱小目标检测、二值图像自动分割、多值图像自动分割及基于内容的图像检索等若干关键问题,针对脉冲耦合神经元结构复杂性及其在图像信号处理中存在阈值反复衰减、自适应性能差和无法自动选择最佳处理结果等一些不足,研究了PCNN模型的机理,并提出改进思路与方法。论文主要内容如下:
1.为有效滤除图像中严重脉冲噪声的干扰,提出了基于脉冲耦合神经网络噪声检测的两级脉冲噪声滤波算法。首先在改进自适应单位连接PCNN(AULPCNN)模型的基础上,利用其同步脉冲发放特性区分定位脉冲噪声点和信号像素点位置,其次根据噪声点局部邻域信息对噪声点进行第1级自适应滤波,然后再对前一级的滤波输出利用具有保护边缘细节特点的多方向信息中值第2级细微辅助滤波。该算法在噪声检测中无需设定检测阈值,噪声检测精度较高;在去噪过程中不但有效滤除噪声干扰,而且能很好地保护图像边缘细节等信息,具有较好的主观视觉效果和客观评价指标,去噪能力强、信噪比高和适应性好,特别是对受严重噪声污染的图像,显示了更大的优越性。
2.针对图像高斯噪声的去除,提出了一种基于改进型脉冲耦合神经网络的双边滤波算法。在考虑图像高斯噪声特征的前提下,引入平滑抑制因子和自适应链接强度,并与相似神经元同步激活特性相结合,形成平滑抑制自适应连接PCNN(SIAL-PCNN)模型,然后应用在含噪图像预滤波迭代处理中,在滤除极值噪声的同时形成反映图像空时信息的赋时矩阵,最后将生成的赋时矩阵信息运用在双边滤波中,并对其进行了自适应性改进与滤除高斯噪声的处理。该算法在较好保护图像边缘细节等信息的情况下,能有效地滤除平滑区域噪声,在信噪比和去噪能力方面都有一定的提高。
3.从含单一弱小目标图像特征出发,提出了结合灰度熵变换的脉冲耦合神经网络小目标图像检测算法。该方法在对含随机噪声和有复杂背景的图像进行非线性灰度熵变换滤波的基础上,考虑灰度熵值映射图在满足目标背景比先验概率的条件下,利用局部最小交叉熵判据,自动选取包含单一小目标局部窗口作为处理图像区域,并进行改进型PCNN迭代检测处理。该算法能自动可靠地检测出复杂背景及随机噪声干扰下的弱小目标。
4.为自动对图像进行二值分割,提出了一种新的自适应迭代全局阈值图像自动分割算法。首先对二维超模糊集隶属函数进行自适应修正,并将其引入到图像超模糊熵概念中,然后从适应图像分割角度考虑,将传统脉冲耦合神经网络模型改进为具有单调指数上升阈值函数的单位链接脉冲耦合神经网络(ULPCNN)抑制捕获模型,最后把ULPCNN与最大超模糊熵判据相结合对图像进行自动分割。该算法能自动确定最佳分割阈值,对图像目标划分清晰、细节保持较好,改善了图像的分割性能。
5.考虑原始图像与分割图像之间的相互关系,以最大互信息为分割目标,以互信息熵差作为一种新的分类判据,在对传统脉冲耦合神经网络模型改进的基础上,提出了一种基于最大互信息改进型PCNN多值图像自动分割算法。该算法能够自动确定最佳分割迭代次数及最佳分割灰度类数,对分割图像具有良好的特征划分能力,且在分割类数较少的情况下,能较好地保持图像细节、纹理及边缘等信息,对图像分割精度高,具有较强的适用性。
6.为简单有效地提取图像重要特征信息,从而更好地提高检索图像的精度,提出了一种基于脉冲耦合神经网络的图像归一化转动惯量(NMI)特征提取及检索算法。首先利用改进简化PCNN模型相似神经元同步时空特性及指数衰降机制,将图像分解为一系列具有相关性的二值图像,然后提取能反映原图像目标形状、结构分布的系列二值图像的一维NMI特征矢量信号,并将其应用在图像检索中。同时,考虑到系列二值图像间的相关性及不同图像间NMI序列值的差异性,引入了马氏距离结合Pearson积矩相关法的综合相似性度量方法。所提算法对图像特征矢量序列具有良好抗几何畸变不变特性及对图像表述的唯一性,且有较好的图像检索效果。
Pulse Coupled Neural Networks (PCNN) , which is provided with analog-digital mixed processing, series-parallel mixed processing and dynamic adaptive processing technologies,is a novel space-time coding artificial neural network developed on basis of the mechanism of biological visual system. Since the characteristics of dynamic threshold, nonlinear modulation coupling, synchronous pulse bursts, dynamic pulse bursts and space-time summation, PCNN is emphasized in numerous fields such as image processing, automatic target recognition, combinatorial optimization, and artificial life at home and abroad.
This paper discusses several key problems of image process, such as impulse noise and Gaussian noise filtering, small target detection, automatic segmentation of binary images, automatic segmentation of multi-threshold images and content-based image retrieval. According to the complex structure of pulse coupled neurons and its defects in image process, such as repeat attenuation of threshold, low adaptive capacity and lack of the automatic selection ability of optimal processing result, this paper studies the model principle of PCNN, proposes improved method, and makes some progresses as follows:
1. In order to effectively remove high dense impulse noise in images, a novel two-stage impulse noise filtering algorithm based on improved PCNN noise detection is put forward. Firstly, on the basis of improved adaptive unit-linking PCNN (AULPCNN), the noisy points and signal points are distinguished and located using the synchronous pulse burst property of AULPCNN, and then the noisy points are smoothed by the first adaptive filter based on their local adjacent information of the noisy points. Secondly, the assistant filtering using the second multi-direction information median filter, which can protect edges details, is adopted. The proposed algorithm needs no detection threshold, which has higher accuracy in the noise detection, the impulse noise can be effectively removed, and edges and details can be preserved well. Meanwhile, the de-noise images obtained by the proposed algorithm have better objective quality and subjective vision effect than other filtering algorithm. This algorithm presents higher signal to noise ratio, stronger capability to reduce noise and better adaptive, and more advantage especially to the high noisy polluted image.
2. Aim at the removal of image Gaussian noise in images, bilateral filtering algorithm based on improved PCNN is put forward. Firstly, on the basis of the characteristics of Gaussian noise in images, the smooth inhibitory factor and adaptive linking strength is introduced, combined with the synchronized activation of similar neurons, and then the adaptive linking PCNN with smooth inhibitory factor (SIAL-PCNN) model is formed. Secondly, the noisy image is processed by using SIAL-PCNN pre-de-noise iteration. The extreme value noise is removed and the time matrix that can reflect image spatial-temporal information is also generated. Finally, the time matrix is operated in bilateral filtering which is given adaptive improvement and de-noise application. The proposed algorithm can effectively remove noise in smooth region in the case of good preserving image edges and details. Meanwhile, its signal to noise ratio and the capability to reduce noises are better increased.
3. A new method based on PCNN and gray scale entropy,is proposed for image segmentation and detection,starting from the aspect of characteristics of single small target image.Based on nonlinear gray scale entropy transform on an image with complex background and stochastic noise,this algorithm takes into account the condition that the mapping images of gray scale entropy satisfy the prior probability of the object to background ratio,and select the local region including a single small target which can be regarded as image processing part.Iterative detection using improved PCNN is utilized under the criterion of local minimum cross-entropy.The novel method can detect small target with the disturbance of complex background and random noise reliably.
4.In order to process the binary segmentation of an image automatically, a new adaptive iterative image segmentation algorithm with the property of global threshold is proposed. Firstly, the membership function is adaptively modified and introduced into the concept of image ultra-fuzzy entropy on the basis of analysis and discussion of two-dimension ultra-fuzzy sets theory. Secondly, the traditional PCNN model is improved into the restraining capture PCNN model with the monotony exponential raised threshold function from the point of view of image segmentation. Finally, the improved ULPCNN is combined with the criterion of maximum ultra-fuzzy entropy, which the best image segmentation is processed. The proposed algorithm automatically determines the number of iterative times, chooses the best threshold, separates the objects in the image clearly, preserves most of the details, and enhances the performance of image segmentation.
5. The traditional PCNN model is improved firstly, a new algorithm of multi-threshold image segmentation using improved PCNN based on the maximization of mutual information is put forward according to the relationship between original image and segmented image, which is based on the object of maximal of mutual information and a new measurement criterion for determining the clusters of an image called difference of mutual information. The new algorithm can automatically determine the optimal cyclic iterative times and the optimal number of gray-scale clusters, has a favorable capability to carve up characteristics and maintain the edges, texture and details of images, has higher precision in image segmentation and can be more adaptability.
6.In order to simply and effectively extract the information of important features in the image so as to improve the accuracy of the image retrieval, a novel algorithm of image normalized moment of inertia (NMI) feature extraction and retrieval based on PCNN is put forward. Firstly, the image is segmented into a series of binary correlation images using synchronous spatial-temporal characteristics of similar neurons and exponential attenuation mechanism of improved and simplified PCNN, and then a one-dimensional NMI feature vector signal of the binary series images, which can reflect the target shape and structure of the original image, is extracted, and applied to the image retrieval. Meanwhile, considering the correlation of binary series images and NMI sequence values differences between different images, the method of compounded similarity measurement of the combination of Mahalanobis distance and Pearson product-moment correlation is introduced. The proposed algorithm has good performance of anti-geometric distortions and the uniqueness for different images expression to the vector sequence of image features, and has better image retrieval results.
引文
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