Perception of temporally filtered X-ray fluoroscopy images

For noisy X-ray fluoroscopy image sequences we quantitatively evaluated image quality after digital temporal filtering to reduce noise. Using an experimental paradigm called a reference/test adaptive forced-choice method we compared detectability of stationary low-contrast disks in filtered and unfiltered, computer-generated image sequences. In the first experiment, a low-pass first-order recursive filter used in X-ray fluoroscopy was found to be much less effective at enhancing detectability than predicted from the reduction of display noise variance, a common measurement of filter effectiveness. Detectability was reasonably predicted by a nonprewhitening human-observer model (NPW-HVS) that included an independently determined human temporal-contrast-sensitivity function. In another experiment, designed to test models over a range of temporal frequencies, we used paired high-pass and low-pass temporal filters that both reduced noise variance by 25%. The high-pass filter was artificially applied to the noise only and greatly improved detectability, while the low-pass filter had little effect. The human-observer model quantitatively described the measurements, but classical prewhitening and nonprewhitening signal detectors did not. As compared to the nonprewhitening, spatio-temporal matched filter, human-observer efficiency was low and variable at 2.1%, 2.9%, and 0.06% for 60 frames of unfiltered low-pass and high-pass noise, respectively. As compared to this detector, humans were not very effective at combining information across frames. On the other hand, signal to noise ratios (SNR's) from the human-observer model were comparable to human performance, and efficiencies were reasonably constant at 40%, 52%, and 32%, respectively. We conclude that it is imperative to include human-observer models and experiments in the analysis of noise-reduction filtering of noisy image sequences, such as X-ray fluoroscopy.     

一种基于运动估计的视频降噪算法

为了有效地去除视频序列中的噪声,提高视频图像质量,提出了一种基于运动估计的视频降噪算法。该算法在由视频序列中的当前帧和前后多帧图像构建的图像层中,通过运动估计技术,沿着物体的运动轨迹进行时域帧间滤波。实验结果显示,本文提出的算法充分利用了视频序列的时域信息,可以有效去除视频序列中的噪声,同时可以很好地保持视频图像的细节。     

Integrating recursive minimum tracking and codebook-based noise estimation for improved reduction of non-stationary noise

Conventional single-channel noise reduction algorithms typically have problems with non-stationary noise. Popular algorithms such as minimum statistics or voice-activity-detector-based methods rely on the assumption that the noise spectral characteristics change very slowly over time. Codebook-based approaches try to overcome this problem by incorporating a priori knowledge about speech and different noise types. These approaches perform a joint estimation of the speech and noise spectra on a frame-by-frame basis. The frames are typically 20-40 ms long so that fast fluctuations of the signal characteristics can be tracked instantaneously. However, these methods require a pitch estimator to prevent speech distortion as well as residual noise in voiced speech frames. In addition, they are not very robust against model mismatch. In this paper, we propose an integrated noise estimation algorithm that combines the ability of codebook-based algorithms to track non-stationary noise with the robustness of a recursive minimum-tracking-based noise estimation algorithm. An objective and subjective evaluation is provided. Results confirm the superiority of the proposed algorithm in non-stationary noise scenarios compared to state-of-the-art algorithms.     

Nonlinear recursive smoothing filters and their use for noise floor estimation

Nonlinear recursive (NLR) digital smoothing filters are introduced and their use for spectrum noise floor estimation described. NLR filters outperform linear filters, achieving comparable results to median filters, and are very simple to implement.<>     

Bayesian motion estimation and interpolation in interlaced videosequences

Motion estimation in video is a very important tool for such applications as interpolation, motion compensation, and segmentation. However, in interlaced video, it is often desirable to perform interpolation prior to the motion estimation. In this correspondence, a novel technique is presented that, rather than minimizing the interpolation error in each of the frames used for motion estimation, seeks to make these errors equal. The new method is shown to perform deinterlacing better than several other approaches also based on Bayesian estimation.     

用于非合作信号噪底估计的改进自回归算法

非合作接收中由于信道或接收机模拟器件的影响可能导致接收信号功率谱存在噪声基底不平坦现象,为便于划定检测门限需要先估计噪声基底再对原始功率谱进行修正.传统的非线性自回归滑动滤波算法采用固定阈值,仅适用于窄带单信号的噪底估计.改进算法通过增加调节系数实现对阈值的动态调节,并根据功率谱特征计算初始阈值和调节系数,提高了原算法的适应范围和工程实用性.仿真结果表明,改进算法对不同种类的信号具有普适性,能解决原算法无法适应的宽带多信号噪底估计.     

Bayesian detection and estimation of cisoids in colored noise

The problem of estimating the number of cisoids in colored noise is addressed. It is assumed that the noise can be modeled by an autoregression whose order has also to be estimated. A new criterion is proposed for estimating the number of cisoids and the autoregressive model order, as well as a new algorithm for estimating the cisoidal frequencies. In the derivation, a Bayesian methodology and subspace decomposition are employed. The proposed criterion significantly outperforms the popular MDL and AIC as applied in a paper by Nagesha and Kay. In addition, an algorithm that reduces the computational complexity of the solution is developed, computer simulations that demonstrate the performance of the criterion are included     

有色噪声下基于Wishart随机矩阵的贝叶斯时变信道估计

提出一种适用于有色噪声环境下的贝叶斯时变信道估计方法,该方法根据Wishart随机矩阵理论和Gibbs采样方法,首先对有色噪声的协方差阵和信道参数进行初估计,在此基础上,对序贯蒙特卡洛(SMC,sequential monte carlo)采样器的参考分布进行改进,使用SMC方法对时变信道参数进行盲跟踪(无需导频信号),从而实现了有色噪声下时变信道的半盲估计.与以往的时变信道估计方法相比,该方法具有估计误差小、顽健性强等特点.计算机仿真结果验证了该方法的有效性.     

A Bayesian framework for noise covariance estimation using the facet model.

In image processing literature, thus far, researchers have assumed the perturbation in the data to be white (or uncorrelated) having a covariance matrix sigma2I, i.e., assumption of equal variance for all the data samples and that no correlation exists between the data samples. However, there have been very few attempts to estimate noise characteristics under the assumption that there is a correlation between data samples. In this work, we propose a new and a novel approach for the simultaneous Bayesian estimation of the unknown colored or correlated noise (population) covariance matrix and the hyperparameters of the covariance model using the well-known facet model. We also estimate the facet model coefficients. We use the facet model because of its simple, yet elegant, mathematical formulation. We use the generalized inverted Wishart density as the prior model for the noise covariance matrix. We place a structure on the covariance matrix using the parameters of a correlation filter. These hyperparameters are estimated by a new extension of the expectation-maximization algorithm called the generalized constrained expectation maximization algorithm that we developed.     

An iterative algorithm for X-ray CT fluoroscopy

X-ray computed tomography fluoroscopy (CTF) enables image guidance of interventions, synchronization of scanning with contrast bolus arrival, and motion analysis. However, filtered backprojection (FB), the current method for CTF image reconstruction, is subject to motion and metal artifacts from implants, needles, or other surgical instruments. Reduced target lesion conspicuity may result from increased image noise associated with reduced tube current. In this report, the authors adapt the row-action expectation-maximization (EM) algorithm for CTF. Because time-dependent variation in images is localized during CTF, the row-action EM-like algorithm allows rapid convergence. More importantly, this iterative CTF algorithm has fewer metal artifacts and better low-contrast performance than FB     

Video compression with binary tree recursive motion estimation andbinary tree residue coding

Binary tree predictive coding (BTPC) is an efficient general-purpose still-image compression scheme, competitive with JPEG for natural image coding and with GIF for graphics. We report the extension of BTPC to video compression using motion estimation and compensation techniques which are simple, efficient, nonlinear and predictive. The new methods, binary tree recursive motion estimation coding (BTRMEC), and binary tree residue coding (BTRC) exploit the hierarchical structure of BTPC, in the first case giving progressively refined motion estimates for increasing numbers of pels and in the second case providing efficient residue coding. Compression results for BTRMEC and BTBC are compared against conventional block-based motion compensated coding as provided by MPEG. They show that both BTRMEC and BTRC are efficient methods to code video sequences.     

A design framework for hybrid approaches of image noise estimation and its application to noise reduction

Noise estimation is an important process in digital imaging systems. Many noise reduction algorithms require their parameters to be adjusted based on the noise level. Filter-based approaches of image noise estimation usually were more efficient but had difficulty on separating noise from images. Block-based approaches could provide more accurate results but usually required higher computation complexity. In this work, a design framework for combining the strengths of filter-based and block-based approaches is presented. Different homogeneity analyzers for identifying the homogeneous blocks are discussed and their performances are compared. Then, two well-known filters, the bilateral and the non-local mean, are reviewed and their parameter settings are investigated. A new bilateral filter with edge enhancement is proposed. A modified non-local mean filter with much less complexity is also present. Compared to the original non-local mean filter, the complexity is dramatically reduced by 75% and yet the image quality is maintained.     

Bayesian algorithms for simultaneous structure from motion estimation of multiple independently moving objects.

In this paper, the problem of simultaneous structure from motion estimation for multiple independently moving objects from a monocular image sequence is addressed. Two Bayesian algorithms are presented for solving this problem using the sequential importance sampling (SIS) technique. The empirical posterior distribution of object motion and feature separation parameters is approximated by weighted samples. The first algorithm addresses the problem when only two moving objects are present. A singular value decomposition (SVD)-based sample clustering algorithm is shown to be capable of separating samples related to different objects. A pair of SIS procedures is used to track the posterior distribution of the motion parameters. In the second algorithm, a balancing step is added into the SIS procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. By using the proposed algorithms, the relative motions of all the moving objects with respect to the camera can be simultaneously estimated. Both algorithms have been tested on synthetic and real-image sequences. Improved results have been achieved.     

Level-set-based motion estimation algorithm for multiple reference frame motion estimation

Motion estimation (ME) has a variety of applications in image processing, pattern recognition, target tracking, and video compression. In modern video compression standards such as H.264/AVC and HEVC, multiple reference frame ME (MRFME) is adopted to reduce the temporal redundancy between successive frames in a video sequence. In MRFME, the motion search process is conducted using additional reference frames, thereby obtaining better prediction signal as compared to single reference frame ME (SRFME). However, its high computational complexity makes it difficult to be utilized in real-world applications. In order to reduce the computational complexity of MRFME, this paper proposes a level-set-based ME algorithm (LSME) without any penalty in the rate-distortion (RD) performance. First, the proposed algorithm partitions the motion search space into multiple level sets based on a rate constraint. The proposed algorithm then controls the ME process on the basis of the predetermined level sets. Experimental results show that the proposed algorithm reduces the ME time by up to 83.46% as compared to the conventional full search (FS) algorithm.     

A new recursive filter for systems with multiplicative noise

An optimal linear recursive minimum mean-square-error estimator was previously developed by the authors (see IEEE Trans. Autom. Control, vol.34, no.5, p.568-74, May 1989) for a zero-mean signal corrupted by multiplicative noise in its measurement model. This recursive filter cannot be obtained by the recursive structure of a conventional Kalman filter where the new estimate is a linear combination of the previous estimate and the new data. Instead, the recursive structure was achieved by combining the previous estimate with recursive innovation, a linear combination of the most recent two data samples and the previous estimate. In this work the signal is extended to be nonzero-mean. In the conventional Kalman filter, the superposition principle can be applied to both the signal and the measurement models for this nonzero-mean extension. However, when multiplicative noise exists, the measurement model becomes nonlinear. Therefore, a new recursive structure for the innovation process needs to be developed to achieve a recursive filter     

Soft Bayesian recursive interference cancellation and turbodecoding for TDD-CDMA receivers

A novel receiver is proposed for CDMA links with recursive turbo coding, based on a soft-input soft-output multistage interference canceler (IC) and an iterative turbo decoder (TD). After performing IC and TD a first time, the coder state estimates available from the two soft-output Viterbi algorithms in the last TD iteration are exploited to calculate the data bits and the redundancy bits. These are employed for preliminary hard cancellation in the first iteration of a supplementary IC run, and then the TD is run again. An alternative solution with soft feedback is obtained by approximating the reliabilities of the redundancy bits with those of the coder state estimates, and using them for preliminary soft cancellation. Simulation results show the effectiveness of the proposed solutions     

Some new algorithms for recursive estimation in constant linear systems

Recursive least-squares estimates for processes that can be generated from finite-dimensional linear systems are usually obtained via ann times nmatrix Riccati differential equation, wherenis the dimension of the state space. In general, this requires the solution ofn(n + 1)/2simultaneous nonlinear differential equations. For constant parameter systems, we present some new algorithms that in several cases require only the solution of less than2nporn(m + p)simultaneous nonlinear differential equations, wheremandpare the dimensions of the input and observation processes, respectively. These differential equations are said to be of Chandrasekhar type, because they are similar to certain equations introduced in 1948 by the astrophysicist S. Chandrasekhar, to solve finite-interval Wiener-Hopf equations arising in radiative transfer. Our algorithms yield the gain matrix for the Kalman filter directly without having to solve separately for the error-covariance matrix and potentially have other computational benefits. The simple method used to derive them also suggests various extensions, for example, to the solution of nonsymmetric Riccati equations.     

A recursive maximum likelihood algorithm for ARMA spectral estimation

A spectral estimation technique is presented for autoregressive moving-average (ARMA) processes. The technique is based on a parameter estimation technique known as the rec ursive maximum likelihood (RML) method. The recursive spectral estimation algorithm is presented and its asymptotic properties are discussed. Simulation results are presented to illustrate the performance of the estimator for various types of data.     

Method for switching noise reduction

A method is proposed for reducing digital noise in mixed analogue-digital CMOS circuits. The method is based on a distributed clock driver and reverse clocking technique. It is best suited to circuits where speed can be traded for noise reduction. Reduction factors depend on the circuit design and speed limitations; values in the range 10-50 can be achieved in most cases     

On a recursive formula for the moments of phase noise

We present a recursive formula for the moments of phase noise in communication systems. The phase noise is modeled using continuous Brownian motion. The recursion is simple and valid for an arbitrary initial phase value. The moments obtained bp the recursion are used to calculate approximations to the probability density function of the phase noise, using orthogonal polynomial series expansions and a maximum entropy criterion