Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors

Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an independent noise. Assuming that the noise-free component has some local mixture distribution (MD), and the noise is either Gaussian or Rayleigh, we derive the minimum mean squared error (MMSE) and the averaged maximum ${a quad posteriori}$ (AMAP) estimators for noise reduction. We use Gaussian and Laplacian MD for each noise-free wavelet coefficient to characterize their heavy-tailed property. Since we estimate the parameters of the MD using the expectation maximization (EM) algorithm and local neighbors, the proposed MD incorporates some information about the intrascale dependency of the wavelet coefficients. To evaluate our spatially adaptive despeckling methods, we use both real medical ultrasound and synthetically introduced speckle images for speckle suppression. The simulation results show that our method outperforms several recently and the state-of-the-art techniques qualitatively and quantitatively.      

应用小波分析对图像进行降噪处理

对传统傅里叶变换与小波变换进行理论分析比较,运用小波分析中小波变换具有对信号的自适应特性可以消除实际信号噪声中的高频成分,通过使用Matlab-6.51中的Wavelet工具箱可实现对图片的降噪操作,最终达到降噪效果。     

Speckle removal from SAR images in the undecimated wavelet domain

Speckle reduction is approached as a minimum mean-square error (MMSE) filtering performed in the undecimated wavelet domain by means of an adaptive rescaling of the detail coefficients, whose amplitude is divided by the variance ratio of the noisy coefficient to the noise-free one. All the above quantities are analytically calculated from the speckled image, the variance and autocorrelation of the fading variable, and the wavelet filters only, without resorting to any model to describe the underlying backscatter. On the test image Lena corrupted by synthetic speckle, the proposed method outperforms Kuan's local linear MMSE filtering by almost 3-dB signal-to-noise ratio. When true synthetic aperture radar (SAR) images are concerned, empirical criteria based on distributions of multiscale local coefficient of variation, calculated in the undecimated wavelet domain, are introduced to mitigate the rescaling of coefficients in highly heterogeneous areas where the speckle does not obey a fully developed model, to avoid blurring strong textures and point targets. Experiments carried out on widespread test SAR images and on a speckled mosaic image, comprising synthetic shapes, textures, and details from optical images, demonstrate that the visual quality of the results is excellent in terms of both background smoothing and preservation of edge sharpness, textures, and point targets. The absence of decimation in the wavelet decomposition avoids typical impairments often introduced by critically subsampled wavelet-based denoising.     

A Bayesian filtering technique for SAR interferometric phase fields

SAR interferograms are affected by a strong noise component which often prevents correct phase unwrapping and always impairs the phase reconstruction accuracy. To obtain satisfactory performance, most filtering techniques exploit prior information by means of ad hoc, empirical strategies. In this paper, we recast phase filtering as a Bayesian estimation problem in which the image prior is modeled as a suitable Markov random field, and the filtered phase field is the configuration with maximum a posteriori probability. Assuming the image to be residue free and generally smooth, a two-component MRF model is adopted, where the first component penalizes residues, while the second one penalizes discontinuities. Constrained aimulated annealing is then used to find the optimal solution. The experimental analysis shows that, by gradually adjusting the MRF parameters, the algorithm filters out most of the high-frequency noise and, in the limit, eliminates all residues, allowing for a trivial phase unwrapping. Given a limited processing time, the algorithm is still able to eliminate most residues, paving the way for the successful use of any subsequent phase unwrapping technique.     

Sideband noise of a large phase carrier in interferometric sensors

Interferometric sensors using balanced detectors and phase stabilisation schemes remain susceptible to laser intensity noise if phase carrier techniques are employed. An intensity stability of 4 × 10?6 is required for the low-frequency side-band noise to be equivalent to microradian phase shift sensitivity if the phase carrier has a peak phase shift of ?/2 rad.     

Modeling heterogeneous network traffic in wavelet domain

Heterogeneous network traffic possesses diverse statistical properties which include complex temporal correlation and non-Gaussian distributions. A challenge to modeling heterogeneous traffic is to develop a traffic model which can accurately characterize these statistical properties, which is computationally efficient, and which is feasible for analysis. This work develops wavelet traffic models for tackling these issues. We model the wavelet coefficients rather than the original traffic. Our approach is motivated by a discovery that although heterogeneous network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are all “short-range” dependent. Therefore, a simple wavelet model may be able to accurately characterize complex network traffic. We first investigate what short-range dependence is important among the wavelet coefficients. We then develop the simplest wavelet model, i.e., the independent wavelet model for Gaussian traffic. We define and evaluate the (average) autocorrelation function and the buffer loss probability of the independent wavelet model for fractional Gaussian noise (FGN) traffic. This assesses the performance of the independent wavelet model, and the use of which for analysis. We also develop (low-order) Markov wavelet models to capture additional dependence among the wavelet coefficients. We show that an independent wavelet model is sufficiently accurate, and a Markov wavelet model only improves the performance marginally. We further extend the wavelet models to non-Gaussian traffic through developing a novel time-scale shaping algorithm. The algorithm is tested using real network traffic and shown to outperform FARIMA in both efficiency and accuracy. Specifically, the wavelet models are parsimonious, and have a computational complexity O(N) in developing a model from a training sequence of length N, and O(M) in generating a synthetic traffic trace of length M     

Least squares phase unwrapping in wavelet domain

Two-dimensional phase unwrapping is an important processing step in some coherent imaging applications. Least squares phase unwrapping is one of the robust techniques used to solve two-dimensional phase unwrapping problems. However, owing to its sparse structure, the convergence rate is very slow, and some practical methods have been applied to improve this condition. A new method for solving the least squares two-dimensional phase unwrapping problem is presented. This technique is based on the multiresolution representation of a linear system using the discrete wavelet transform. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels. Fast convergence in separate coarse resolution levels makes the overall system convergence very fast.     

小波分析在图像降噪中的应用

本文在对图像降噪进行总体概述的基础上,介绍了传统降噪和小波降噪的原理,提出一种以阈值降噪法为基础的混合算法。然后用MATLAB中的小波工具箱对一个含有噪声图像进行降噪。通过实验结果的对比,可以看出新算法可以更为有效地降低噪声,并较好地保留图像的细节。     

SAR interferometric signatures of forest

The potential of SAR interferometry for forest mapping and monitoring is discussed. It is shown that forest can clearly be discriminated from other land categories. Furthermore it is possible to distinguish a number of forest types. The presented approach is based on the SAR interferometric correlation and the backscatter intensities using ERS-1 SAR repeat-pass data. Baseline, time interval, and seasonal dependences were analyzed, substantiating a wide applicability of the approach. Data over an Alaskan test site were used to extend the results found over temperate forest to boreal forest and to demonstrate the potential of the described technique over remote areas. In addition, repeat-pass SAR interferometry was found to be particularly sensitive to change. Examples for the recognition of freezing, mechanical cultivation of agricultural fields, and canopy growth are shown     

SAR speckle reduction using wavelet denoising and Markov random field modeling

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.     

基于小波包分析的ECG信号降噪

在临床医学检测上,心电信号(ECG)是一种典型的人体生理信号,在采集时常伴有工频干扰、基线漂移和肌电干扰等影响。为后续能进行有效诊断,必须对采集的ECG信号进行预处理。文中介绍了一种基于小波包分析的ECG信号降噪方法,首先根据熵标准选择最佳小波包基,然后对ECG信号进行多层小波包分解,最后对分解系数进行阈值量化后得到重构信号。通过对胎儿ECG信号的仿真实验,结果表明,小波包分析方法比傅里叶变换和小波变换等具有更好的降噪效果。     

小波分析在航天系统降噪处理中的应用

航天系统噪声是航天系统设备的顽疾,它干扰有用信号的调制、发射、传输、接收和解调,严重影响数据的精度。为了解决航天系统设备存在的噪声问题,结合小波降噪原理,提出了基于小波分析的降噪处理方法。利用Matlab软件设计了小波降噪程序,并对降噪过程进行了仿真。结果表明,基于小波分析的降噪处理方法可有效降低航天系统设备噪声。     

小波包域值法去除合成孔径雷达图像斑点噪声

合成孔径雷达(SAR)图像的斑点噪声使图像的解译能力明显降低,影响了SAR图像的应用,去除图像斑点噪声是SAR图像处理及应用的前提。去除的方法在图像域有很多,在频率域,也有基于FFT变换和小波变换的许多方法。文中根据沙漠地区的SAR图像的特点,对基于图像域和频率域的几种滤波方法进行了比较,提出了一种基于小波包变换的SAR图像斑点噪声去除方法,结果表明该方法既去除了斑点噪声,又保持了边缘特征信息。     

Dual learning based compression noise reduction in the texture domain

Compression noise reduction is similar to the super-resolution problem in terms of the restoration of lost high-frequency information. Because learning-based approaches have proven successful in the past in terms of addressing the super-resolution problem, we focus on a learning-based technique for compressed image denoising. In this process, it is important to search for the exact prior in a training set. The proposed method utilizes two different databases (i.e., a noisy and a denoised database), which work together in a complementary way. The denoised images from the dual databases are combined into a final denoised one. Additionally, the input noisy image is decomposed into structure and texture components, and only the latter is denoised because most noise tends to exist within the texture component. Experimental results show that the proposed method can reduce compression noise while reconstructing the original information that was lost in the compression process, especially for texture regions.     

Novel passive phase noise cancelling technique for interferometric fibre optic sensors

A novel technique for the cancellation of phase noise in a fibre interferometer caused by laser frequency emission jitter is described. The scheme is based on the use of a reference interferometer and phase generated carrier passive homodyne phase detection. Signal processing involving cross-multiplication of the sine and cosine components of the sensor and reference interferometer outputs is shown to provide cancellation of phase noise.<>     

Oscillator stability and phase noise reduction in phase lockedactive microstrip patch antenna

A phase locked loop (PLL) method for controlling a 4 GHz active patch antenna was investigated in order to both frequency stabilise the oscillator and to reduce the phase noise; both these aims were achieved by optimising the PLL parameters. Experimental results showed that a phase noise reduction of up to 26 dB was realised     

A versatile wavelet domain noise filtration technique for medical imaging

In this paper, we propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.     

New approaches in interferometric SAR data processing

It is known that interferometric synthetic-aperture radar (SAR) images can be inverted to perform surface elevation mapping. Among the factors critical to the mapping accuracy are registration of the interfering SAR images and phase unwrapping. A registration algorithm is presented that determines the registration parameters through optimization. A figure of merit is proposed that evaluates the registration result during the optimization. The phase unwrapping problem is approached through a new method involving fringe line detection. The algorithms are tested with two SEASAT SAR images of terrain near Yellowstone National Park. These images were collected on SEASAT orbits 1334 and 1420, which were very close together in space, i.e. less than 100 m     

Simulation of interferometric SAR response for characterizing thescattering phase center statistics of forest canopies

A coherent scattering model for tree canopies is employed in order to characterize the sensitivity of an interferometric SAR (INSAR) response to the physical parameters of forest stands. The concept of an equivalent scatterer for a collection of scatterers within a pixel, representing the vegetation particles of tree structures, is used for identifying the scattering phase center of the pixel whose height is measured by an INSAR. Combining the recently developed coherent scattering model for tree canopies and the INSAR Δk-radar-equivalence algorithm, accurate statistics of the scattering phase-center location of forest stands are obtained numerically for the first time. The scattering model is based on a Monte Carlo simulation of scattering from fractal-generated tree structures, and therefore is capable of preserving the absolute phase of the backscatter. The model can also account for coherent effects due to the relative position of individual scatterers and the inhomogeneous extinction experienced by a coherent wave propagating through the random collection of vegetation particles. The location of the scattering phase center and the correlation coefficient are computed using the Δk-radar equivalence simply by simulating the backscatter response at two slightly different frequencies. The model is successfully validated using the measured data acquired by JPL TOPSAR over a selected pine stand in Raco, MI. A sensitivity analysis is performed to characterize the response of coniferous and deciduous forest stands to a multifrequency and multipolarization INSAR in order to determine an optimum system configuration for remote sensing of forest parameters     

Dual-loop integer PLL for phase noise reduction

Analog Integrated Circuits and Signal Processing - In this work, a dual-loop integer PLL is proposed aimed at improving the overall phase noise performance at the PLL output. The main loop is a...