基于LBM对流扩散方程的水平集图像分割方法

为使图像处理更快速稳定,文中通过研究偏微分方程的元胞自动机模型,提出了一种基于格子波尔兹曼模型（LBM）对流扩散方程的图像分割算法：用对流扩散方程替换水平集方程,利用LBM求解对流扩散方程,实现基于LBM的水平集图像分割。实验分析表明,该方法能精确求解对流扩散方程,算法模型能够取得闭合的分割曲线,同样能够很好的处理图像拓扑结构的变化。并且通过与水平集方法和窄带水平集方法进行计算速度实验对比,可证明该算法大大减少了分割的计算量。     

基于各向异性扩散的图像去噪并放大

本文从各向异性扩散图像去噪方法出发,在偏微分方程有限元方法的基础上,提出一种图像复原并放大的 算法,实验表明该算法比中值滤波后插值所得图像的主、客观质量要好。     

电子散斑干涉条纹处理偏微分方程方法的回顾与展望

介绍了偏微分方程(PDE)图像处理的基本原理,构造偏微分方程模型的变分法和常见的滤波模型,并给出常见滤波模型的能量泛函。回顾了近几年偏微分方程图像处理技术在电子散斑干涉(ESPI)条纹处理中的应用成果,包括应用偏微分方程图像处理方法同时实现电子散斑干涉条纹图和相位图的滤波和增强处理,提出用于密集电子散斑干涉条纹处理的方向偏微分方程模型及提取电子散斑干涉条纹骨架线的偏微分方程方法。介绍了这些方法与传统方法相比的优势,并进一步展望了偏微分方程图像处理在光测技术中的发展趋势。     

一种改进的基于高斯曲率和偏微分方程的图像降噪算法

偏微分方程的图像降噪算法是一种简单有效的方法.在降噪的同时有效地保持图像的重要特征,如边缘、角点、细节等是一个重要问题.将图像的几何特征与偏微分方程的降噪算法结合起来,将图像看成三维空间的二维平面.基于高斯曲率能够有效地保持图像的重要特征,提出了一种基于高斯曲率的图像降噪的改进算法,该算法能够得到一个稳态的非平凡解,从而能够避免中止时间的选取.最后的仿真实验表明,本文算法的有效性.     

基于正则化变分模型的SAR图像增强方法

讨论合成孔径雷达（SAR）图像的噪声抑制与分辨率增强问题.建立偏微分方程抑噪方法与正则化点增强方法相结合的正则化变分模型,该模型同时具有偏微分方程模型的抑噪优势和正则化模型的分辨率增强优势.在图像的背景区域采用偏微分方程模型进行噪声抑制,而在图像的目标区域,先采用后向扩散方程进行锐化,然后再采用正则化模型进行分辨率增强,使整幅图像的处理结果均得到优化.此外,在偏微分方程抑噪模型的构造上,结合SAR成像的工程背景,提出了基于SAR图像幅度信息的前向-后向扩散方程,使方程能有效抑制图像背景区域的噪声并锐化目标边缘.大量的试验结果表明该方法能有效增强目标的强散射点,显著抑制噪杂波区的噪声.     

一种新型的图像修复方法

在当今对信息的需求不断增加的情况下,作为获取信息的重要手段,图像处理显得尤为重要.以往的用于图像修复的BSCB模型体现了偏微分方程在图像处理领域中的广泛应用和重要性,然而其也具有一定的缺陷,将针对BSCB模型运算速度过慢的问题,分析其产生的原因,并在此基础上提出一种新的算法.实验证明,新方法在处里的效果上强于传统模型,且修复所需时间明显缩短.     

基于一维自组织神经网络的图像数据融合算法研究

多传感器数据融合技术最近在图像处理领域得到广泛的重视和应用.传统图像处理是典型的二维信号处理,图像数据融合也属此范畴.为了融合来自同一景物的多幅带有不同噪声的图像,本文提出了一种基于一维自组织特征映射神经网络的图像融合算法,利用等灰度图法来确定自组织映射神经网络的聚类数,并用一维的像素特征量直接进行二维图像信号处理. 仿真试验给出的详细结果表明了该图像融合算法的有效性.     

以TV模型为例的西藏壁画数字图像修复技术研究

通过介绍图像修复技术中的一个主要修复模型,即基于偏微分方程的图像修复技术,具体分析了数字图像的的破损类型及对应方法,在偏微分方程的基础上,深入了解此模型中具有代表性的TV图像修复模型.分析TV模型在噪声影响下对修复方程的约束,实现对噪声图像的处理方法.分析和对比此类模型对不同破损图像的修复效果,得出了最佳修复方案.     

基于MSFM的快速图像修补算法

近年来,基于非线性高阶偏微分方程的高质量图像修补算法已经得到了发展,但这些方法需要大量的迭代,时间开销大,复杂度高.Telea提出的基于FMM的修补算法可以快速完成修补,但存在行进方向和边缘信息保持的问题.对此进行了改进,采用MFM方法,并引入扩散张量.实验结果表明提出的方法可以达到较高的质量,而且速度快.     

FPGA在图像处理中的应用

随着多媒体应用已经普及到千家万户,相应的数字图像处理技术已成熟且在图像通信等领域得到日益广泛的应用.然而图像处理的技术有很多种,本文给出了FPGA在图像处理中的简单应用,把FPGA应用到图像处理中可以提高图像处理的速度,减少占用逻辑单元.     

Orthogonal-directional forward diffusion image inpainting and denoising model

In this paper, an orthogonal-directional forward diffusion Partial Differential Equation （PDE） image inpainting and denoising model which processes image based on variation problem is proposed. The novel model restores the damaged information and smoothes the noise in image simultaneously. The model is morphological invariant which processes image based on the geometrical property. The regularization item of it diffuses along and cross the isophote, and then the known image information is transported into the target region through two orthogonal directions. The cross isophote diffusion part is the TV （Total Variation） equation and the along isophote diffusion part is the inviscid Helmholtz vorticity equation. The equivalence between the Helmholtz equation and the inpainting PDEs is proved. The model with the fidelity item which is used in the whole image domain denoises while preserving edges. So the novel model could inpaint and denoise simultaneously. Both theoretical analysis and experiments have verified the validity of the novel model proposed in this paper.     

An efficient framework for image/video inpainting

Image inpainting has been widely applied to many applications, such as restoring corrupted old photos, erasing video logos, concealing errors in a digital video processing system, and so on. However, traditional geometric inpainting methods suffer low efficiency. To tackle this problem, this paper addresses an efficient transform based framework for geometric methods. Given an image, we firstly decompose it, then separately perform restoration process and finally employ Laplacian diffusion function to hold local texture coherence. Experimental results show that the proposed method not only speeds up and enhances the performances of geometric methods, but also obtains a better restoration results compared with the traditional texture and hybrid methods.     

Review of Methods of Image Segmentation Based on Quantum Mechanics

The quantum theory application is a hot research area in recent years, especially the theory of quantum mechanics. In this paper, we focus on the research of image segmentation based on quantum mechanics. Firstly, the theory of quantum mechanics is introduced; afterwards, a review of image segmentation methods based on quantum mechanics is presented; and finally, the characteristics about the quantum mechanics applied to image processing are concluded. Two main research topics are discussed in this paper. One is to emphasize that quantum mechanics can be applied in different research areas, such as image segmentation, and the second is to conclude some methods in image segmentation and give some suggestions for possible novel methods by applying quantum mechanics theory. As a summary, this is a review paper which presents some methods based on the feasible theory in quantum mechanics aiming at achieving a better performance in image segmentation.     

Improved image deblurring based on salient-region segmentation

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm.     

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.     

Oriented speckle reducing anisotropic diffusion.

Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver.     

各向异性扩散图像去噪现状研究

介绍了基于偏微分方程(Partial Differential Equations,PDE)各向异性扩散图像去噪的P&M数学模型及各种改进方法,以及引入结构张量的PDE去噪模型和冲击滤波PDE去噪模型,分析各种模型的优缺点,为扩散模型的设计提供了参考。揭示了各向异性扩散去噪与贝叶斯最大后验估计(MAP)去噪的内在联系,通过扩散系数的计算分析几类常用去噪模型的保边性能。阐述了一类特殊PDE去噪模型(总变分去噪模型)与小波阈值去噪之间的相关性,从而可以通过小波系数的范数近似求解一些特殊的变分问题,避免复杂的非线性求解过程。最后对各向异性扩散图像去噪的未来发展进行了展望。     

双层约束下基于局部和全局信息的图像插值新模型

该文提出一种双层约束的图像插值模型,模型在原始未插值图像梯度模约束下同时基于局部和全局信息处理。使用偏微分方程处理边缘像素,锐化边缘同时平滑边缘块状效应;平滑区域像素点的插值操作使用非局部均值模型,非局部均值模型通过对原始图像全局信息加权平均得到待处理图像像素值,图像平滑。使用双层约束模型处理纹理图像可以保持纹理特征,平滑纹理部分线形特征位置的块状效应。最后理论和实验结果证明使用双层控制模型可以直接将噪声图像插值放大。