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《现代电子技术》2015,(8)
针对边缘检测算法存在的检测精度与抑噪的矛盾,提出一种基于新的图像边缘检测算法。算法将检测窗口按照0o,45o,90o和135o四个不同方向分别划分为两个子区域,先统计每个检测窗口(3×3)内脉冲噪声点的个数,如果超过3个,则扩大检测窗口至5×5。对于检测窗口每个方向划分的两个子区域,分别计算区域内的非噪声点的平均灰度值,利用平均值差的绝对值作为窗口的方向梯度值,进而求得中心点的梯度。然后,对梯度图像采用改进的非极大值抑制方法进行细化,并提取边缘。实验结果表明,该算法检测的图像边缘方向性较强,边缘较细,不仅对不同程度脉冲噪声干扰图像具有较强的抑噪能力,而且对高斯噪声也具有一定程度的抑制效果,算法具有较强的适应性。 相似文献
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Because corruption of image by White-Gaussian noise is a frequently encountered problem in acquisition, transmission and processing of image, and classical edge detection operators such as Roberts, Sobel, Prewitt and LOG operator have the deficiency of being sensitive to White-Gaussian noise, this paper proposes a new edge detection algorithm for Image corrupted by White-Gaussian noise that can reasonably consider White-Gaussian noise reduction and correct location of edge, and provides its specific arithmetic process. Finally, the comparison based on principle of new edge detection algorithm and classical edge detection operator is done, the experimental results indicate that the performance of new edge detection algorithm is better than that of classical edge detection operator. 相似文献
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传统的边缘检测算子对灰度图像进行边缘检测时存在图像细节被丢失,边界不连续等问题。针对上述问题,提出一种基于数学形态学和最小均方差滤波相结合的图像边缘检测方法,该算法先利用小均方差滤波的方法可以有效地滤除图像中的噪声,然后利用形态学中的腐蚀运算对图像进行边缘检测处理。实验结果表明:该方法能够有效地去噪,精确地检测图像中的细节,并且边界的连续性好。 相似文献
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Fan-beam collimators are used in single photon emission computed tomography to improve the sensitivity for imaging of small organs. The disadvantage of fan-beam collimation is the truncation of projection data surrounding the organ of interest or, in those cases of imaging large patients, of the organ itself producing reconstruction artifacts. A spatially varying focal length fan-beam collimator has been proposed to eliminate the truncation problem and to maintain good sensitivity for the organ of interest. The collimator is constricted so that the focal lengths of the holes vary across the face of the collimator with the shortest focal lengths at the center and the longer focal lengths at the periphery of the collimator. The variation of the focal length can have various functional forms but in the authors' work it is assumed to increase monotonically toward the edge of the collimator. A backprojection filtering reconstruction algorithm is derived for this type of collimation. The algorithm first backprojects the projections, then performs a two-dimensional filtering. The algorithm is efficient and has been tested via computer simulations. 相似文献
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Shing-Chow Chan Yue-Xian Zou 《Signal Processing, IEEE Transactions on》2004,52(4):975-991
This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other. 相似文献
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In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection (SA-BCS-SPL-ED) image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform (DCT), dual-tree discrete wavelet transform (DDWT), discrete wavelet transform (DWT) and Contourlet (CT) are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber (BCS-SPL), the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images. 相似文献
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Hossein-Zadeh GA Ardekani BA Soltanian-Zadeh H 《IEEE transactions on medical imaging》2003,22(7):795-805
An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data. 相似文献
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Standard linear diversity combining techniques are not effective in combating fading in the presence of non-Gaussian noise. An adaptive spatial diversity receiver is developed for wireless communication channels with slow, flat fading and additive non-Gaussian noise. The noise is modeled as a mixture of Gaussian distributions and the expectation-maximization (EM) algorithm is used to derive estimates for the model parameters. The transmitted signals are detected using a likelihood ratio test based on the parameter estimates. The new adaptive receiver converges rapidly, its bit error rate performance is very close to optimum when relatively short training sequences are used, and it appears to be relatively insensitive to mismatch between the noise model and the actual noise distribution. Simulation results are included that illustrate various aspects of the adaptive receiver performance 相似文献
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A novel class of nonlinear filters for image processing is proposed. This class is a combination of nonlinear mean and order statistic filters. Median, homomorphic, α-trimmed mean, nonlinear mean, order statistic, and linear filters can be considered as special cases of this class. The properties of these filters in the presence of different kinds of noise are investigated. It is shown that these filters can be used for the reduction of additive white noise, signal-dependent noise, and impulse noise. It is also shown that they preserve edges better than linear filters. Such filters can successfully be used as edge detectors, by appropriate adjustment of some of their parameters. Edge information can be used as an input to these filters to perform in an adaptive manner, changing their behaviour near the edges of an image. It is finally shown that many of the filters proposed have a reasonable (and in certain cases small) computational complexity. 相似文献
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Tai-Kuo Woo 《Communications Letters, IEEE》2001,5(3):81-84
The fast convergence rate and its immunity to the eigenvalue spread of the input correlation matrix make the RLS algorithm particularly attractive. However, the computational complexity is high. We propose using a hierarchical approach to reduce the computational complexity and further increase the convergence rate. The results of simulation runs and theoretical justifications confirm our claims 相似文献
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目标信号混入训练数据时,自适应空域滤波的权值在距离维实时更新会造成信号自相消的现象。针对这一问题,提出一种基于两级检测处理的全距离维更新计算权值的方法。第一级处理进行样本选择,将强目标信号检测出来,并剔除出训练数据;第二级处理进行自适应波束形成和目标检测。仿真结果说明提出的方法能够在整个距离维更新自适应权值,适合于警戒雷达对抗旁瓣干扰。 相似文献
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Yuen S.M. Abend K. Berkowitz R.S. 《Antennas and Propagation, IEEE Transactions on》1988,36(5):629-635
A multiple-input-multiple-output orthogonalization algorithm and its efficient systolic implementation are presented. The processing architecture is developed using a basic two-input-two-output decorrelation processing element as the primitive building block. Its features are discussed and compared to the approach of K. Gerlach and F.A. Studer (see ibid., vol.AP-34, no.3, p.458-462, 1986) which is based on the modified Gram-Schmidt (MGS) orthogonalization procedure. For simplicity of illustration in the development, batch processing is emphasized. The main features of the newly developed multiple-channel orthogonalization architecture are: (1) it requires no broadcasting of data and any given processing node in the structure only communicates with its neighboring nodes in pipelining fashion; (2) in terms of the total number of arithmetic operations, it is at least as efficient as the MGS approach; (3) the new architecture is developed in a systematic and bottom-up fashion; (4) it is an extremely regular and compact processing structure; (5) no unscrambling of the output channels is needed; and (6) the architecture presented places no restriction on the number of input channels 相似文献
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Barcelos C.A.Z. Boaventura M. Silva E.C. Jr. 《IEEE transactions on image processing》2003,12(7):751-763
An anisotropic nonlinear diffusion equation for image restoration is presented. The model has two terms: the diffusion term and the forcing term. The balance between these terms is made in a selective way, in which boundary points and interior points of the objects that make up the image are treated differently. The optimal smoothing time concept, which allows for finding the ideal stop time for the evolution of the partial differential equation is also proposed. Numerical results show the proposed model's high performance. 相似文献