基于改进Canny算子的图像边缘检测方法

经典Canny图像边缘检测算法在面对复杂背景和椒盐噪声时会出现伪边缘或漏检等问题,影响后续图像分割,目标检测和识别.针对经典Canny算法高斯滤波和人工门限设置2个步骤进行优化改进,首先提出一种循环自适应滤波方法代替高斯滤波对图像进行平滑降噪,提升椒盐噪声抑制性能的同时较好的保留了图像中的细节信息,然后提出一种最小类内类间距准则的2-均值算法自动确定高低阈值门限,相对于人工门限设置方法具有更高的精确性和更强的适应性.基于标准图像库数据开展试验,结果表明所提方法可以明显提升经典Canny算法的椒盐噪声鲁棒性和复杂背景下的边缘检测性能.     

广义全变差正则化图像去噪模型研究

本文研究了全变差正则化模型在图像去噪过程中易产生阶梯效应的问题,依据图像的局部结构特利用联合高斯滤波器和边缘检测算子的方法,构建了广义全变差正则化图像去噪模型,获得了在消除噪声的同时能够保留图像边缘细节和纹理信息的结果.实验结果表明,广义全变差正则化模型在平滑噪声的同时能够保留图像的边缘轮廓等细节信息,得到的复原图像在峰值信噪比、平均结构相似度和主观视觉效果方面均有所提高.     

改进Canny算法在钢印数字特征提取中的应用

边缘检测是实现图像分割、特征提取和图像理解的基础.研究了传统Canny算子的优势与不足.在此基础上,提出了一种快速分块自适应Canny算法.方法首先按字符大小分割图像,然后在每一块上进行自适应边缘检测.自适应边缘检测是在平滑图像的同时得到高斯滤波尺度参数,然后采用Otsu方法的自适应阈值计算Canny算子的高、低门限值.实验结果表明,方法不需人工设定参数就能自动提取不同光照背景下的钢印数字边缘,而且能有效抑制噪声,与传统Canny算子相比,边缘连接程度最佳,噪声敏感程度较低,实时性较强.     

基于D-S证据理论的杂草图像边缘检测的研究

为了解决杂草图像边缘检测的不确定性问题,构造出图像边缘的邻域一致性、方向性和结构性三种信息测度统计,利用D-S证据理论对三种测度进行融合来实现分割后杂草图像的边缘检测,实验表明,此算法能够有效的降低噪声的影响,准确的提取出杂草边缘.     

基于小波域的多尺度非参数图像分割方法

主要介绍了一种基于信息熵理论及图像多尺度信息来对图像进行非参数主动轮廓模型分割的有效方法.由于小波多分辨率特性的引入,可以最大程度地利用图像多尺度信息以确保分割的准确性和完整性.又由于小波变换的特性,低频信息的使用更是进一步降低了噪声影响.文中把图像分割问题定义为在分割区域边缘长度满足一定约束条件下,图像标记场与各个尺度图像像素值之间的互信息熵最大化过程.该方法可以有效地降低噪声对于分割的影响,及确保分割的准确性和完整性.     

基于Canny算子边缘检测的小波阈值去噪方法

本文研究了信号处理中图像去噪的问题.利用小波变换理论提出了一种基于Canny算子边缘检测的小波阈值去噪方法,实验结果表明,该方法在有效去除噪声的同时能够更好地保留图像的边缘.     

一种结合小波模极大值法和Canny算子的目标提取方法

针对基于小波变换的目标提取中忽略低频子图像的一些重要信息的问题.提出了一种基于小波变换的模极大值法和Canny算子的目标提取方法.在小波域中,通过求解局部小波系数模型的极大值点提取(检测)高频边缘,利用Canny算子提取(检测)低频边缘.然后根据融合规则对两个子图像边缘进行融合.实验结果表明,该方法不仅能有效地增强图像边缘,而且能准确地定位图像边缘.     

一种去除图像中Cauchy噪声的滤波算法

数字图像在采集、传输等过程中会产生各种噪声,噪声特征不同,处理方法也不同,如何尽可能恢复被强噪声干扰的图像是一个有意义的研究课题,因为传统的滤波算法在强噪声情况下,难以得到理想的结果.该文在中值滤波的基础上,结合局部区域内像素聚类的思想,提出一种对图像边缘进行修正的滤波算法,对三幅被Cauchy噪声干扰的图像处理的结果表明,该算法弥补了中值滤波在细节处理方面的不足,和其它方法相比,在滤除强噪声并保护图像细节和边缘方面有明显提高,是一种有效的去除强噪声的滤波算法.     

一种基于脉冲噪声的新型迭代滤波算法

在局部极值噪声检测和迭代中值滤波的基础上,基于图像结构和脉冲噪声的特征分析,有效结合局部极值检测和幅度差阈值、梯度差阈值的检测方法,提出了一种基于噪声检测的迭代脉冲噪声滤除算法.并通过仿真实验和算法评价,验证了该算法不仅能够达到很好的去噪效果,而且在保留图像细节信息方面也取得了一定的成效.     

基于模糊中值滤波的椒盐噪声去除方法

研究基于模糊中值滤波的椒盐噪声去除方法。通过比较图像各像素点的灰度值,定义基于图像梯度信息的各点被判别为噪声点的模糊隶属函数。利用此模糊隶属函数对中值滤波方法进行加权,得到了一种加权中值滤波器,可实现边缘处椒盐噪声的有效滤除。讨论这种模糊加权方法与其它先进滤波方法的结合途径,指出了其推广应用价值。最后利用数值实验验证本文方法的有效性,结果表明,相比于自适应中值滤波方法,本文方法得到的滤波图像在峰值信噪比及结构相似度方面均有明显提高。     

Application of new advanced CNN structure with adaptive thresholds to color edge detection

Color edge detection is much more efficient than gray scale detection when edges exist at the boundary between regions of different colors with no change in intensity. This paper presents adaptive templates, which are capable of detecting various color and intensity changes in color image. To avoid conception of multilayer proposed in literatures, modification has been done to the CNN structure. This modified structure allows a matrix C, which carries the change information of pixels, to replace the control parts in the basic CNN equation. This modification is necessary because in multilayer structure, it faces the challenge of how to represent the intrinsic relationship among each primary layer. Additionally, in order to enhance the accuracy of edge detection, adaptive detection threshold is employed. The adaptive thresholds are considered to be alterable criteria in designing matrix C. The proposed synthetic system not only avoids the problem which is engendered by multi-layers but also exploits full information of pixels themselves. Experimental results prove that the proposed method is efficient.     

Local edge detectors using a sigmoidal transformation for piecewise smooth data

For piecewise smooth data, edges can be recognized by jump discontinuities in the data. Successful edge detection is essential in digital signal processing as the most relevant information is often observed near the edges in each segmented region. In this paper, using the concentration property of existing local edge detectors and the clustering property of sigmoidal transformations, we provide enhanced edge detectors which diminish the oscillations of the local detector near jump discontinuities as well as highly improve rate of convergence away from the discontinuities. Numerical results of some examples illustrate efficiency of the presented method.     

Image Denoising by a Local Clustering Framework

Images often contain noise due to imperfections in various image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in the human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines—for example, medical science—image denoising has become an important research area. In the literature, there are many different types of image denoising techniques, most of which aim to preserve image features, such as edges and edge structures, by estimating them explicitly or implicitly. Techniques based on explicit edge detection usually require certain assumptions on the smoothness of the image intensity surface and the edge curves which are often invalid especially when the image resolution is low. Methods that are based on implicit edge detection often use multiresolution smoothing, weighted local smoothing, and so forth. For such methods, the task of determining the correct image resolution or choosing a reasonable weight function is challenging. If the edge structure of an image is complicated or the image has many details, then these methods would blur such details. This article presents a novel image denoising framework based on local clustering of image intensities and adaptive smoothing. The new denoising method can preserve complicated edge structures well even if the image resolution is low. Theoretical properties and numerical studies show that it works well in various applications.     

基于微分算子的边缘检测法在基因芯片图像识别中的应用

一种基于梯度算子和拉普拉斯算子检测医学基因芯片荧光数字图像边缘的方法．数字图像经过图像增强等预处理以后，突出了我们感兴趣的数字图像特征，根据数字图像灰度变化的趋势，用灰度变化的一阶导数(梯度算子)检测图象中的边缘区域；用灰度变化的二阶导数确定图像边缘的中心位置．最后根据某些预定的准则把满足这些准则的象素组成一条边缘．     

Segmentation of Pulmonary Nodule Images Using 1-Norm Minimization

Total variation minimization (in the 1-norm) has edge preserving and enhancing properties which make it suitable for image segmentation. We present Image Simplification, a new formulation and algorithm for image segmentation. We illustrate the edge enhancing properties of 1-norm total variation minimization in a discrete setting by giving exact solutions to the problem for piecewise constant functions in the presence of noise. In this case, edges can be exactly recovered if the noise is sufficiently small. After optimization, segmentation is completed using edge detection. We find that our image segmentation approach yields good results when applied to the segmentation of pulmonary nodules.     

The Topological Gradient Method: From Optimal Design to Image Processing

The aim of this article is to review and extend the applications of the topological gradient to major image processing problems. We briefly review the topological gradient, and then present its application to the crack localization problem, which can be solved using the Dirichlet to Neumann approach. A very natural application of this technique in image processing is the inpainting problem, which can be solved by identifying the optimal location of the missing edges. Edge detection is of extreme importance, as edges convey essential information in a picture. A second natural application is then the image reconstruction. A class of image reconstruction problems is considered that includes restoration, demosaicing, segmentation and super-resolution. These problems are studied using a unified theoretical framework which is based on the topological gradient method. This tool is able to find the localization and orientation of the edges for blurred, low sampled, partially masked, noisy images. We review existing algorithms and propose new ones. The performance of our approach is compared with conventional image reconstruction processes.     

Iterative adaptive RBF methods for detection of edges in two-dimensional functions

Radial basis functions have gained popularity for many applications including numerical solution of partial differential equations, image processing, and machine learning. For these applications it is useful to have an algorithm which detects edges or sharp gradients and is based on the underlying basis functions. In our previous research, we proposed an iterative adaptive multiquadric radial basis function method for the detection of local jump discontinuities in one-dimensional problems. The iterative edge detection method is based on the observation that the absolute values of the expansion coefficients of multiquadric radial basis function approximation grow exponentially in the presence of a local jump discontinuity with fixed shape parameters but grow only linearly with vanishing shape parameters. The different growth rate allows us to accurately detect edges in the radial basis function approximation. In this work, we extend the one-dimensional iterative edge detection method to two-dimensional problems. We consider two approaches: the dimension-by-dimension technique and the global extension approach. In both cases, we use a rescaling method to avoid ill-conditioning of the interpolation matrix. The global extension approach is less efficient than the dimension-by-dimension approach, but is applicable to truly scattered two-dimensional points, whereas the dimension-by-dimension approach requires tensor product grids. Numerical examples using both approaches demonstrate that the two-dimensional iterative adaptive radial basis function method yields accurate results.     

Using Conventional Edge Detectors and Postsmoothing for Segmentation of Spotted Microarray Images

Segmentation of spotted microarray images is important in generating gene expression data. It aims to distinguish foreground pixels from background pixels for a given spot of a microarray image. Edge detection in the image processing literature is a closely related research area, because spot boundary curves separating foregrounds from backgrounds in a microarray image can be treated as edges. However, for generating gene expression data, segmentation methods for handling spotted microarray images are required to classify each pixel as either a foreground or a background pixel; most conventional edge detectors in the image processing literature do not have this classification property, because their detected edge pixels are often scattered in the whole design space and consequently the foreground or background pixels are not defined. In this article, we propose a general postsmoothing procedure for estimating spot boundary curves from the detected edge pixels of conventional edge detectors, such that these conventional edge detectors together with the proposed postsmoothing procedure can be used for segmentation of spotted microarray images. Numerical studies show that this proposal works well in applications.

Datasets and computer code are available in the online supplements.