基于小波包的红外/被动毫米波图像特征融合

红外/被动毫米波(IR/PMMW)复合制导是当前发展多模复合制导技术的热点方向.红外探测系统在低能见度条件下的穿透能力不如被动毫米波,而被动毫米波探测图像分辨率不如红外图像.为了更好地识别目标的轮廓信息,提出一种新的基于小波包边缘检测的特征级主成分融合方法.新方法先用小波包边缘检测方法检测出包含水平边缘、垂直边缘和对角边缘的边缘图像,然后对边缘图像进行小波包去噪,再用主成分融合方法进行图像特征级的融合,最后用阈值方法提取出融合后的边缘.实验仿真结果表明,与传统的小波及小波包边缘检测方法相比,新方法融合后的边缘图像更容易分辨出目标的轮廓信息.     

改进的基于小波变换的图像边缘检测方法的研究

本文介绍一种基于小波变换模极大值进行图像边缘检测的方法。对图像进行二维小波变换,其梯度模值反映了图像的边缘,用这种方法可以检测到图像所有边缘的细节,但同时也会检测到一些伪边缘和噪声点。本文采用图像分块方法确定阈值,并用该阈值来限定模值,与传统边缘检测方法相比,可以得到更好的边缘检测效果。     

一种基于阈值分割的红外图像边缘检测方法

针对红外图像对比度和信噪比低,经典的图像边缘检测方法对实际图像难以检测的特点,提出了一种基于阈值分割的边缘检测算法。首先利用最大方差阈值法分割出红外图像的目标图像,其次用线性拉伸的方法对目标图像中存留的噪声进行去除,最后运用Sobel算子对目标图像进行边缘检测得到结果图像,并与经典的Roberts边缘检测算法、Sobel检测算法、Prewwit检测算法、Gauss-Laplacian检测算法进行比较。实验结果显示该检测方法是可行、有效的。     

对数域梯度与改进Sobel算子相结合的边缘检测

为了消除不均匀光照给图像边缘检测带来的影响,提出一种对数域梯度与改进Sobel算子相结合的边缘检测方法。该方法首先将图像变换到对数域,通过削弱低频入射分量增强高频反射分量,以提升图像亮度;然后合成改进的4方向Sobel算子,并用无穷范数表示梯度,以使需要被检测的图像更加完整;最后通过Bernsen算法得到的阈值与高斯滤波后得到的Bernsen算法阈值进行线性组合来确定最佳阈值,以使图像边缘更加连续完整。仿真实验结果表明,该方法可以有效消除不均匀光照对图像边缘检测的影响,与相关算法和文献相比,该方法对不同照度图像的边缘检测效果更好。     

太赫兹成像三维块匹配自适应Canny边缘检测算法

针对传统Canny算子在滤波时会模糊边缘且需要人工设置高低阈值的缺点，提出了一种基于三维块匹配的改进自适应阈值Canny边缘检测算法，并用于太赫兹三维层析成像。该算法一方面对滤波方法进行了改进，用三维块匹配(BM3D)滤波算法结合引导滤波算法代替高斯滤波算法以减少图像边缘信息的丢失；另一方面，针对传统人工设定阈值的不确定性，将梯度图进行块匹配后对三维图像块组使用最大类间方差法(OTSU)以自适应确定高低阈值。最后利用该算法对含有噪声的图像进行边缘检测处理，发现在高斯噪声方差为20时滤波后的峰值信噪比(PSNR)从22.202提升至27.151，验证了该算法去除噪声的有效性。三维块匹配改进自适应阈值Canny边缘检测算法(BM-OTSU-Canny)减少了错误边缘的数量，同时保留了连接性较好的边缘点，改善了边缘细节信息的提取效果。     

一种改进的基于小波变换的图像边缘提取方法

基于现有的小波去噪方法和边缘提取方法,提出了一种通过自适应阈值设定的小波边缘提取算法,从而改进了传统方法中通过固定阈值进行小波边缘检测中存在的问题,通过对相关图像的分析结果可以看出,该方法能够较好地对图像边缘进行自适应检测,达到了较好的效果.     

基于最优阈值的叶类中药边缘检测算法研究

提出了一种利用最优阈值分割和形态学处理相结合的边缘检测方法,首先通过Otsu最优阈值算法分割数字图像,然后利用形态学腐蚀运算腐蚀图像,最后利用分割后图像与腐蚀过的图像的相减获得边缘.该方法实现了对叶类中药显微图像效果更优的边缘检测,同时对比了传统边缘检测算法对叶类中药显微图像的图像边缘检测效果,为下一步的叶类中药显微图像的分割与自动识别奠定基础.     

小波变换的自适应阈值图像边缘检测方法

在Marr的计算机视觉系统中，图像边缘检测占据着重要位置。但由于问题本身的复杂性和技术手段的限制，图像边缘检测的研究困难重重。近10年来，由于小波分析技术在工具和数学方法上的重大突破，试图将小波理论应用于图像边缘检测。根据边缘检测的评价标准，参照最佳边缘滤波器的设计要求，确定选择用于边缘检测的小波母函数的一般准则，并在此基础上构造出二次B样条小波，提出了基于小波变换的自适应阈值图像边缘检测的新方法。通过计算机仿真对该算法进行了验证，结果成于采用固定阈值的小波边缘检测。     

复杂背景下扩展目标多尺度小波分割策略

用大尺度小波检测主要轮廓边缘及抑制背景细纹和噪声,用小尺度小波进行边缘的精确定位检测,并分别计算自适应的双阈值;对用较大阈值限幅输出的2幅边缘图像进行相与,并去掉短的离散短边缘得到组合边缘输出图;结合在较小阈值限幅输出的大尺度小波边缘检测出图像及其相位图,对有相近幅度和相位的边缘进行连接,得到最后的边缘分割结果。实验结果表明,该分割策略是有效的。     

基于改进Canny算子的垃圾图像边缘检测

为提高垃圾识别分类的准确率,文中在垃圾图像预处理过程中提出了一种基于改进Canny算子的垃圾图像边缘检测方法。该方法从传统Canny算子滤波方式、梯度方向及阈值自适应3个角度实现了垃圾图像边缘检测的优化。针对Canny算子高斯滤波仅适用于高斯噪声和边缘细节易丢失的问题,采用改进的梯度倒数加权法进行滤波。针对Canny算子易检测出伪边缘的问题,通过在计算图像梯度方向的过程中增加方向梯度模板实现了边缘的精确化。同时采用最小误差法解决人工设定阈值的局限性,实现阈值自适应。实验结果表明,该方法在去噪性能和边缘细节两方面得到了改进,获得了更好的边缘检测效果,为后续垃圾图像的识别分类提供了技术保障。     

Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

Image super-resolution by curve fitting in the threshold decomposition domain

A new curve-fitting scheme is proposed in this paper to produce super-resolution images from a single low-resolution source image. The most unique feature of this method is that the threshold decomposition is performed on the given source image to obtain multiple binary images so that the curve-fitting applied on each resulted binary image can be made very efficient and accurate, thus allowing us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large up-scaling factor is used. Two novel techniques are further proposed to improve the visual quality: (1) a spreading technique (applied on some significant pixels detected in each threshold decomposed binary image) is used to remove ladder-like false edges that often appear visually in super-resolution images, and (2) an edge correction (guided by the edge information extracted from the original source image) is used to sharpen all inherent edges. Our results are compared with those achieved by using the state-of-arts techniques, showing the ability of our algorithm to achieve a better visual quality in smooth areas as well as for sharp edges and small objects.     

Edge Preserving Interpolation of Digital Images Using Fuzzy Inference

This paper presents a novel edge preserving interpolation method for digital images. This new method reduces drastically the blurring and jaggy artifacts at the high-contrast edges, which are generally found in the interpolated images using conventional methods. This high performance is achieved by two proposed operations: a fuzzy-inference based edge preserving interpolator and a highly oblique edge compensation scheme developed based on an edge orientation detector. The former synthesizes the interpolated pixels to match the image local characteristics. Hence, edge sharpness can be retained. However, due to the small footage of the fuzzy interpolation method, it cannot avoid edge jaggedness along the highly oblique edges that have very sharp angles against one of the coordinates. Therefore, a segment matching technique is developed to identify precisely the orientation of the highly oblique edges. Combining these two techniques, we improve significantly the visual quality of the interpolated images, particularly at the high-contrast edges. Both the synthesized images (such as letters) and the natural scenes (captured by camera) have been tested and the results are very promising.     

Convolution-Based Edge Detection for Image/Video in Block DCT Domain

This paper presents a scheme for performing convolution operation directly on compressed images without decompressing them first. The use of such a scheme is demonstrated and discussed by showing the implementation of the Laplacian-of-Gaussian operator for edge detection. We present a complete evaluation of the different parameters involved in this process and show edge detection results on several real images through our proposed scheme. In each case, it is shown that the proposed scheme of directly performing convolution on the compressed data leads to not only a significant computation speedup but also yields better edges.     

Standardization of edge magnitude in color images.

Edge detection is a useful task in low-level image processing. The efficiency of many image processing and computer vision tasks depends on the perfection of detecting meaningful edges. To get a meaningful edge, thresholding is almost inevitable in any edge detection algorithm. Many algorithms reported in the literature adopt ad hoc schemes for this purpose. These algorithms require the threshold values to be supplied and tuned by the user. There are many high-level tasks in computer vision which are to be performed without human intervention. Thus, there is a need to develop a scheme where a single set of threshold values would give acceptable results for many color images. In this paper, an attempt has been made to devise such an algorithm. Statistical variability of partial derivatives at each pixel is used to obtain standardized edge magnitude and is thresholded using two threshold values. The advantage of standardization is evident from the results obtained.     

Weighted local variance-based edge detection and its application to vascular segmentation in magnetic resonance angiography

Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. These robustness and capabilities are essential for detecting the boundaries of vessels in low contrast regions of images, which can contain intensity inhomogeneity, such as bias field, interferences induced from other tissues, or fluctuation of the speed related vessel intensity. The performance of the WLV-based edge detection scheme is studied and shown to be able to return strong and consistent detection responses on low contrast edges in the experiments. The proposed edge detection scheme can be embedded naturally in the active contour models for vascular segmentation. The WLV-based vascular segmentation method is tested using MRA image volumes. It is experimentally shown that the WLV-based edge detection approach can achieve high-quality segmentation of vasculatures in MRA images.     

An edge detection algorithm based on the multi-direction shear transform

Wavelet multi-resolution analysis allows us to detect edges at different scales. However, the wavelet transform can only capture edge information in three directions, horizontal, vertical and diagonal. In addition, the extracted edges are discontinuous. A new edge detection method to solve these problems is proposed in his paper. Firstly, the image is extended symmetrically by applying horizontal and vertical reflections. Secondly, shear transform is taken on the extended images according to various shear matrixes. Thirdly, the edges of the sheared images are detected by means of wavelet transform. The edges detected in different directions have some difference and can complement each other, so we fuse them with a fusion rule. Finally, a threshold is set to refine the edges. The proposed method works efficiently on the images, and the continuity of the edge is getting better. Besides, the method is able to distinguish the real edges from the noise.     

一种基于自适应双向扩散过程的无源毫米波图像去噪与增强

研究和设计了一种新型自适应双向扩散过程,对无源毫米波图像进行降噪和增强处理.根据图像的局部特征进行自适应扩散处理,在图像的同质区域进行各向同性扩散降低噪声,在图像的边缘处沿着图像边缘的切向方向进行正向扩散降低噪声,沿着图像边缘的法向方向进行反向扩散锐化边缘.通过对仿真图像和实测的91.5 GHz无源毫米波图像的实验表明...     

Adaptive edge image enhancement based on maximum fuzzy entropy

Edges carry the most i mportant information in i ma-ges.Ini mages,edges are marked with discontinuities orsignificant variations in intensity or gray levels,provi-dingthe locations of objects'contours[1].Many edge i m-ages that are processed by some gradi…