一种新的自适应小波阈值SAR图像滤波算法

为有效降低乘性斑点噪声对合成孔径雷达（SAR）图像的影响,提出了一种新的基于小波系数广义高斯分布（GGD）模型的自适应阈值估计去噪算法。首先分析了经对数变换的SAR图像小波系数的统计分布特性,然后提出了子带自适应阈值估计方法,通过对数变换,将该算法应用于含斑点噪声的SAR图像去噪。仿真图像和真实SAR图像的实验结果表明,该算法同目前流行的其他阈值算法相比,运算复杂度低,算法高效,并且在保留原始图像重要细节特征和图像后向散射特性的同时,显著地减少相干斑噪声。     

分区均值的自适应噪声图像边缘检测算法

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。     

基于类间方差的图像Canny边缘提取改进

到目前为止,在边缘检测方面,Canny算子因为它优良的边缘检测特性,应用在很多方面。然而它也有局限性,传统Canny算子高低阈值是通过人工凭借经验来选取,且低阈值固定是高阈值的一半,这就使得它在一些特殊领域无法应用,且实际中图像易受光照、杂散光以及其他各种噪声的干扰。因此针对传统Canny算子阈值确定的困难性,针对性地提出了利用双重Otsu方法确定高低阈值的方法,此方法利用两次Otsu方法分别确定高阈值和低阈值。     

基于数学形态学和LMS滤波的图像边缘检测算法研究

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。     

基于均值梯度的脉冲噪声图像边缘检测算法

为了有效地检测出受脉冲噪声污染图像的边缘,提出了一种基于均值梯度的图像边缘检测算法。算法将检测窗口根据水平和垂直方向分成上、下、左、右4个不同区域,先计算每个区域内非噪声点的平均灰度值,然后利用这些值的差分计算图像梯度,得出梯度图像,最后对梯度图像采用了改进的非极大值抑制方法对梯度图像进行细化并提取边缘。实验结果表明,该算法能够较好地检测出受较高密度脉冲噪声干扰的图像边缘,而且边缘较细,效果明显优于传统Sobel算法,具有较强的实用性。     

抗噪形态学边缘检测新算子

提出了一种基于抗噪形态学算子的新算法。该算法大大增强抗噪形态学算子的抗噪能力,并且实现对同时混有椒盐噪声和高斯噪声的图像进行噪声滤除。实验过程中使用仅含噪声的图像进行算子的仿真,使结果更清晰、直观。实验结果表明,该算子有良好的抗噪能力,对噪声的大小和浓度要求都比较小,鲁棒性较强。     

A backprojection filtering algorithm for a spatially varying focal length collimator

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.     

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

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.     

Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection

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.     

Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering

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.     

An adaptive spatial diversity receiver for non-Gaussianinterference and noise

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     

HRLS: a more efficient RLS algorithm for adaptive FIR filtering

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     

An efficient algorithm and systolic architecture for multiplechannel adaptive filtering

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     

Iterate-averaging sign algorithms for adaptive filtering with applications to blind multiuser detection

Motivated by the developments on iterate averaging of recursive stochastic approximation algorithms and asymptotic analysis of sign-error algorithms for adaptive filtering, this work develops two-stage sign algorithms for adaptive filtering. The proposed algorithms are based on constructions of a sequence of estimates using large step sizes followed by iterate averaging. Our main effort is devoted to improving the performance of the algorithms by establishing asymptotic normality of a suitably scaled sequence of the estimation errors. The asymptotic covariance is calculated and shown to be the smallest possible. Hence, the asymptotic efficiency or asymptotic optimality is obtained. Then variants of the algorithm including sign-regressor procedures and constant-step algorithms are studied. The minimal window width of averaging is also dealt with. Finally, iterate-averaging algorithms for blind multiuser detection in direct sequence/code-division multiple-access (DS/CDMA) systems are proposed and developed, and numerical examples are examined.     

A well-balanced flow equation for noise removal and edge detection

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.     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

Applications of adaptive filtering to ECG analysis: noisecancellation and arrhythmia detection

Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.     

一种有效的自适应语音降噪算法及实现

提出了一种改进的基于离散Hartley变换(DHT)自适应滤波算法(DHT—LMS),将其应用于电话通信噪声抵消系统,并进行了实验研究,其结果表明,将该算法应用于自适应语音降噪系统,能获得较好的降噪效果,且基于信号处理器TMS320LF2407上开发实现简单,系统结构紧凑、可靠性高。     

一种实时抗噪的运动区域检测算法

运动区域检测是运动目标分割的基础,检测的准确程度直接决定运动目标的提取精度.针对复杂背景下运动区域难以准确而又实时检测的问题,提出一个新的运动区域检测方法.首先分析了帧差图像的噪声模型,引入三倍标准差原理,给出运动区域的判别标准,然后定义了一个窗口密集度公式用以去除孤立噪声点.实验证明,相较于几种比较典型的运动检测方法,该方法在抗噪性和实时性方面都表现了较好的性能.     

A skin tone detection algorithm for an adaptive approach to steganography

Challenges face biometrics researchers and particularly those who are dealing with skin tone detection include choosing a colour space, generating the skin model and processing the obtained regions to fit applications. The majority of existing methods have in common the de-correlation of luminance from the considered colour channels. Luminance is underestimated since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in the segregation of skin and non-skin clusters. To this end, here we use a new colour space which contains error signals derived from differentiating the grayscale map and the non-red encoded grayscale version. The advantages of the approach are the reduction of space dimensionality from 3D, RGB, to 1D space advocating its unfussiness and the construction of a rapid classifier necessary for real time applications. The proposed method generates a 1D space map without prior knowledge of the host image. A comprehensive experimental test was conducted and initial results are presented. This paper also discusses an application of the method to image steganography where it is used to orient the embedding process since skin information is deemed to be psycho-visually redundant.