Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

Nonsubsampled contourlet 变换(NSCT ) 为图象提供灵活 multiresolution， anisotropy，和方向性的扩大。与原来的 contourlet 变换相比，它是移动不变的并且能在奇特附近克服 pseudo-Gibbs 现象。脉搏联合了神经网络(PCNN ) 是一个视觉启发外皮的神经网络并且由全球联合和神经原的脉搏同步描绘。它为图象处理被证明合适并且成功地在图象熔化采用。在这份报纸， NSCT 与 PCNN 被联系并且在图象熔化使用了充分利用他们的特征。在 NSCT 领域的空间频率是输入与大开火的时间在 NSCT 领域激发 PCNN 和系数作为熔化图象的系数被选择。试验性的结果证明建议算法超过典型基于小浪，基于 contourlet，基于 PCNN，并且 contourlet-PCNN-based 熔化算法以客观标准和视觉外观。

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.

Irregular Segmented Region Compression Coding Based on Pulse Coupled Neural Network

An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.

An improved pulse coupled neural networks model for semantic IoT

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

Unit-Linking PCNN中的彩色图像边缘检测

PCNN模型具有相似群神经元同步发放脉冲的特性,适合于图像分割。对彩色图像的亮度分量进行对数变换,使其更符合人眼的视觉特性;在PCNN进行彩色图像R、G、B三分量分割的过程中,利用遗传算法进行神经元关键参数的选择,利用偏态指标进行迭代控制;在Unit-Linking PCNN模型中实现R、G、B三分量分割图的边缘检测,利用加权合并策略得到最终的边缘检测结果。仿真结果表明,该方法得到的结果体现了图像中更多的轮廓细节,具有很好的自适应性。

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

Filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated that this feature itself is a very good mechanism for image filtering when the image is damaged with pep and salt (PAS) type noise. An adaptive filtering method, in which the noisy pixels are first located and then filtered based on the output of the PCNN, is presented. The threshold function of a neuron in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise contaminated image respectively. The filtered image has no distortion for noisy pixels and only less mistiness for non-noisy pixels, compared with the conventional window-based filtering method. Excellent experimental results show great effectiveness and efficiency of the proposed method, especially for heavy-noise contaminated images.

Pulse Coupled Neural Network Edge-Based Algorithm for Image Text Locating

This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.

Image segmentation of embryonic plant cell using pulse-coupled neural networks

Traditional image segmentation algorithms exhibit weak performance for plant cells which have complex structure. On the other hand, pulse-coupled neural network (PCNN) based on Eckhorn’s model of the cat visual cortex should be suitable to the segmentation of plant cell image. But the present theories cannot explain the relationship between the parameters of PCNN mathematical model and the effect of segmentation. Satisfactory results usually require time-consuming selection of experimental parameters. Mean-while, in a proper, selected parametric model, the number of iteration determines the segmented effect evaluated by visual judgment, which decreases the efficiency of image segmentation. To avoid these flaws, this note proposes a new PCNN algorithm for automatically segmenting plant embryonic cell image based on the maximum entropy principle. The algorithm produces a desirable result. In addition, a model with proper parameters can automatically determine the number of iteration, avoid visual judgment,

Saliency Motivated Pulse Coupled Neural Network for Underwater Laser Image Segmentation

The detection range of underwater laser imaging technology achieves 4—6 times of detection range of conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea exploration and robotic works. However, the special features in underwater laser images, such as speckle noise and non-uniform illumination, bring great difficulty for image segmentation. In this paper, a novel saliency motivated pulse coupled neural network(SM-PCNN) is proposed for underwater laser image segmentation. The pixel saliency is used as external stimulus of neurons. For improvement of convergence speed to optimal segmentation, a gradient descent method based on maximum two-dimensional Renyi entropy criterion is utilized to determine the dynamic threshold. On the basis of region contrast in each iteration step, the real object regions are effectively distinguished,and the robustness against speckle noise and non-uniform illumination is improved by region selection. The proposed method is compared with four other state-of-the-art methods which are watershed, fuzzy C-means, meanshift and normalized cut methods. Experimental results demonstrate the superiority of our proposed method to allow more accurate segmentation and higher robustness.

Memristor-based multi-channel pulse coupled neural network for image fusion

Image fusion is widely used in computer vision and image analysis.Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion,a memristor-based multi-channel pulse coupled neural network(M-MPCNN)for image fusion is proposed.Based on a dual-channel pulse coupled neural network(D-PCNN),a novel multi-channel pulse coupled neural network(M-PCNN)is firstly constructed in this paper.Then the exponential growth dynamic threshold model is used to improve the pulse generation of pulse coupled neural network,which can not only avoid multiple ignitions effectively,but can also improve operational efficiency and reduce complexity.At the same time,synchronous capture can also enhance image edge,which is more conducive to image fusion.Finally,the threshold and synaptic characteristics of pulse coupled neural networks(PCNNs)can be well realized by using a memristor-based pulse generator.Experimental results show that the proposed algorithm can fuse multi-source images more effectively than existing state-of-the-art fusion algorithms.

基于快速联合双边滤波器和改进PCNN的红外与可见光图像融合

针对红外与可见光图像融合结果中细节丢失、目标不显著和对比度低等问题,提出了一种结合快速联合双边滤波器(fast joint bilateral filter,FJBF)和改进脉冲耦合神经网络(pulse coupled neural network,PCNN)的红外与可见光图像融合方法,在保证融合图像质量的前提下有效提高运行效率。首先,利用快速联合双边滤波器对源图像进行分解;其次,为了更好地提取图像中显著结构和目标信息,针对基础层图像采用一种基于视觉显著图(visual significance map,VSM)的加权平均融合规则,针对细节层图像采用改进脉冲耦合神经网络模型进行融合,其中PCNN的所有参数都可以根据输入波段自适应调节;最后,将基础层融合图与细节层融合图叠加重构得到融合图像。实验结果表明,该方法提高了融合图像的效果,有效地保留了目标、背景细节和边缘等重要信息。

正余弦动态干扰哈里斯鹰算法的PCNN参数优化图像融合

哈里斯鹰优化算法存在前期全局开发种群分布不广泛、后期局部开发易陷入收敛精度不够的缺陷,因此提出一种正余弦动态干扰的哈里斯鹰优化算法。首先,在前期的全局开发阶段,对两种不同的进化策略分别采用余弦函数和正弦函数进行鹰群群体分布干扰,从而扩大群体分布范围,强化鹰群初期全局探索阶段的广度,为后期进行局部开发提供更好的条件;然后,在局部开发阶段,通过对猎物逃逸能量公式进行曲线化调整,使得猎物能量损耗与自然界中的真实能量损失更加匹配,进而提升开发阶段的捕获能力;最后,将改进的正余弦动态干扰的哈里斯鹰优化算法对脉冲耦合神经网络(PCNN)的链接输入、时间衰减系数、链接强度3个参数进行优化,并应用于可见光与ToF置信图的图像融合。采用6种对比算法及24个测试函数对改进后的算法进行仿真实验验证,证明了基于正余弦动态干扰的哈里斯鹰优化算法具有较好的寻优能力和更高的收敛精度。通过与其他融合算法进行对比实验,验证了改进后的融合算法相比原始算法的融合效果有显著提升。

结合局部熵与梯度能量的双通道医学图像融合

多模态医学图像融合已成为有效结合正常组织结构和异常改变信息、提高医学诊断效率的强有力辅助技术.针对空域融合技术在处理图像细节丢失、光谱退化等方面的缺陷,提出一种在联合双边滤波器(JBF)域内实现图像结构与细节信息增强的双通道频域多模态医学图像融合方法.该方法将源图像分解为结构和能量2个通道,分别处理图像纹理细节信息和边缘强度信息.在结构通道中,通过改进梯度能量得到局部梯度能量算子,进一步提升融合图像对小尺度细节信息的表达能力和对噪声的鲁棒性;在能量通道中,利用非下采样轮廓波变换提高模型的多方向多尺度特性,并提出一种局部熵细节增强算子和脉冲耦合神经网络结合的高频子带处理框架,达到增强能量通道中结构信息和细节信息的效果.在Atlas公开数据集上,与基于MST、稀疏表示、PCNN以及JBF的6种具有代表性的频域融合方法进行对比及消融实验的结果表明,所提方法的融合图像与源图像相似度提升35.0%,空间频率提升16.2%,边缘保持度提升12.5%,对比度提升11.2%;并在视觉方面有较好的效果,明显优于对比方法.

结构相似度优化的混合多尺度医学图像融合

现有的多模态医学图像融合方法存在结构信息以及相位特征保存不完整的问题,为此,提出一种基于混合多尺度分解和结构相似度优化的医学图像融合方法。首先,针对单一滤波器在保留图像结构和细节方面的局限性,提出一种多尺度分解潜在低秩表示(MDLat LRR)和非下采样轮廓波变换(NSCT)相结合的混合多尺度分解方法,利用MDLat LRR分解源图像获取低秩层和显著层,使用NSCT对低秩层做进一步分解;其次,在基础层上使用基于局部拉普拉斯能量和的融合规则,使融合图像具有更好的视觉效果,对于细节层,通过脉冲耦合神经网络(PCNN)计算全局耦合以获得融合权重,从而融合细节层;最后,考虑到空间一致性,由初始融合图像获取线性调整图像,利用加权局部结构相似度进行测量从而得到修正系数,并对初始融合图像进行修正,提高融合图像中信息的准确性。实验结果表明,相比于MSMG、EMFusion、CFL等9种方法,该方法在归一化互信息、空间频率误差比等10个客观评价指标上评估性能更高,特别在相位一致性、余弦特征互信息以及差异相关和指标上,分别比次优方法平均提升了13.89%、19.62%和35.8%,所提方法的融合图像具有更丰富、更准确的细节信息和良好的视觉效果。

基于剪切波变换和脉冲耦合神经网络的图像融合方法研究

图像融合的目的是将多个来源的图像合并,从而产生1个包含所有原始图像信息的新图像。传统的图像融合方法可能损失一些重要信息或导致图像失真,因此研究新的图像融合方法具有重要意义。作为新兴技术,剪切波变换和脉冲耦合神经网络在图像融合中的应用潜力尚未被充分发掘。文章致力于研究这2种技术在图像融合中的效果,并提出结合两者的新方法。

基于脉冲耦合神经网络的图像分割算法分析

为进一步提高脉冲耦合神经网络(PCNN)在图像分割中的性能,文章研究了基于PCNN模型的图像分割算法,并探讨其模型简化算法,旨在减少其中的参数量,从而提高图像分割处理效率和分割效果。借助Girl属性图像进行仿真验证,通过与OTSU算法及LevelSet算法的分割效果进行对比,文章论证了算法的有效性及可行性。实验结果表明,该算法具有显著的分割效果。

基于全局能量特征与改进PCNN的红外与可见光图像融合

为了改善红外与可见光融合图像存在不清晰、图像对比度低以及缺少纹理细节的问题,本文提出了一种基于参数自适应脉冲耦合神经网络(parameter-adaptive pulse-coupled neural network,PAPCNN)图像融合算法。首先,对源红外图像进行暗通道去雾,增强图像的清晰度;然后,使用非下采样剪切波变换(non-subsampled shearlet transform,NSST)分解源图像,使用全局能量特征结合改进的空间频率自适应权重融合低频系数,将纹理能量作为PA-PCNN外部输入融合高频系数;最后,通过逆NSST变换得到最终融合灰度图像。本文方法与7种经典算法在2组图像中进行对比实验,实验结果表明:本文方法在评价指标中明显优于对比算法,提高了融合图像的清晰度和细节信息,验证了本文方法的有效性。将灰度图像转为伪彩色图像进一步增强了融合图像的辨识度和人眼的感知效果。

基于灰狼优化算法的PCNN中药材显微图像分割

为有效分割中药材显微图像的目标信息,提出了一种基于灰狼优化算法(Gray wolf optimization,GWO)的改进型脉冲耦合神经网络(Pulse coupled neural networks,PCNN)中药材显微图像自动分割方法。首先,从适应处理显微图像的角度出发对传统PCNN模型进行简化与改进;其次,在训练图像中提取香农熵值作为GWO的适应度函数来自适应调节PCNN关键参数——链接系数β,进而实现图像目标的最优分割;最后,将所提算法与聚类分割法、OTSU法、传统PCNN法进行了实验比较,并用骰子系数、体积重叠误差、相对体积、精确度和交并比等常用医学图像分割评判标准对4种处理方法做了客观评价。实验结果表明,所提方法能够实现图像的自适应分割,较好地保持了图像细节、纹理及边缘等信息,对不同显微图像分割准确度高,改善了图像的分割性能,具有较强的适用性。