Parallel image restoration with a two-dimensional likelihood-based algorithm

We describe a pixelwise parallel algorithm for the restoration of images that have been corrupted by a low-pass optical channel and additive noise. This new algorithm is based on an iterative soft-decision method of error correction (i.e., turbo decoding) and offers performance on binary-valued imagery that is comparable to the Viterbi algorithm. We quantify the restoration performance of this new algorithm on random binary imagery for which it is superior to both the Wiener filter and the projection onto convex sets algorithms over a wide range of channels. For typical optical channels, the new algorithm is within 0.5 dB of the two-dimensional Viterbi restoration method [J. Opt. Soc. Am. A 17, 265 (2000)]. We also demonstrate the extension of our new algorithm to correlated and gray-scale images using vector quantization to mitigate the associated complexity burden. A highly parallel focal-plane implementation is also discussed, and a design study is presented to quantify the capabilities of such a VLSI hardware solution. We find that video-rate restoration on 252 x 252 pixel images is possible using current technology.     

基于瞬时频率估计的自适应Vold-Kalman阶比跟踪研究

结合旋转机械启停阶段振动信号的特点,提出了一种基于Viterbi算法和短时傅里叶变换(STFT-VA)的瞬时频率估计算法,STFT-VA算法在高噪声、临近阶比和交叉阶比情况下有较高的精度.实现了基于STFT-VA算法的自适应Vold-Kalman阶比跟踪(AVKF-OT),和传统的以硬件实现的Vold-Kalman阶比跟踪(VKF-OT)相比,此方法具有无需转速计等硬件、用纯软件的方法实现.实验结果表明,该方法能够在时域中准确地提取幅值和复杂频率变化的阶比,适合于复杂旋转机械振动响应特征提取.     

Noise-predictive maximum-likelihood method combined with infiniteimpulse response equalization

An infinite impulse response (IIR) can closely approximate the high density magnetic recording channel response with only a single pole and a small number of zeros. As a consequence, a near-optimal performance can be achieved with the Viterbi algorithm (VA) incorporating a single-tap noise predictor. The number of states in the VA trellis is determined by the number of zeros used in the IIR modeling of the channel response. The single noise-predictor tap corresponds to the single pole in the IIR model. The overall complexity for a given level of performance is smaller with this approach than with the noise-predictive maximum-likelihood (NPML) method based on conventional partial response equalization     

Image restoration with the Viterbi algorithm

The Viterbi algorithm (VA) is known to given an optimal solution to the problem of estimating one-dimensional sequences of discrete-valued pixels corrupted by finite-support blur and memoryless noise. A row-by-row estimation along with decision feedback and vector quantization is used to reduce the computational complexity of the VA and allow the estimation of two-dimensional images. This reduced-complexity VA (RCVA) is shown to produce near-optimal estimation of random binary images. In addition, simulated restorations of gray-scale images show the RCVA estimates to be an improvement over the estimates obtained by the conventional Wiener filter (WF). Unlike the WF, the RCVA is capable of superresolution and is adaptable for use in restoring data from signal-dependent Poisson noise corruption. Experimental restorations of random binary data gathered from an optical imaging system support the simulations and show that the RCVA estimate has fewer than one third of the errors of the WF estimate.     

Communication theoretic image restoration for binary-valued imagery

We present a new image-restoration algorithm for binary-valued imagery. A trellis-based search method is described that exploits the finite alphabet of the target imagery. This algorithm seeks the maximum-likelihood solution to the image-restoration problem and is motivated by the Viterbi algorithm for traditional binary data detection in the presence of intersymbol interference and noise. We describe a blockwise method to restore two-dimensional imagery on a row-by-row basis and in which a priori knowledge of image pixel correlation structure can be included through a modification to the trellis transition probabilities. The performance of the new Viterbi-based algorithm is shown to be superior to Wiener filtering in terms of both bit error rate and visual quality. Algorithmic choices related to trellis state configuration, complexity reduction, and transition probability selection are investigated, and various trade-offs are discussed.     

Multilevel spatial modulation

In this paper, a new Multilevel Spatial Modulation technique is proposed. It combines computationally efficient multilevel oding and spatial modulation based on trellis codes to increase coding gain, diversity gain, and bandwidth efficiency. The trellis complexity of the single-stage system increases exponentially, whereas in the proposed multilevel system the complexity increases linearly. The proposed system is analyzed with optimal Viterbi and suboptimal sequential decoding algorithms. The results show that sequential decoding saves 75% of the computational power with a loss of 2 dB SNR approximately, when compared with optimal Viterbi decoding, over both fast- and slow-fading channel conditions. Since the antenna index is used as a source of information in spatial modulation, the number of antennae required increases with the throughput and packing a large number of antennas make cross-correlation unavoidable. In this paper, a low complexity modified decoding technique is also proposed for the multilevel spatial modulation system, in which the correlated received signals are equally combined and decoded by the multistage decoder using the Viterbi algorithm. This technique exploits the receiver antenna correlation and makes the decoding complexity independent of number of antennas. The simulation results indicate that the proposed low complexity algorithm gives approximately 8–10 dB gain when compared with optimal Viterbi decoder with equivalent computational complexity when the eight highly correlated signals are equally combined. This may be a suitable solution for mobile handsets where size and computational complexity are the major issues.     

Detection techniques for high-density magnetic recording

In this paper, we present techniques for improving the performance of Viterbi detector in the presence of channel nonlinearities and media noise, which are the dominant source of errors in high density magnetic recording channels. Instead of treating these distortions separately, we combine them into a "high-density (HD) noise", which is signal dependent and correlated in nature. To compensate for signal-dependent mean and correlation of HD noise, we modify the branch metrics by subtracting an estimate of the mean from the signals on each branch and optimize the equalization target by minimizing the dominant error event probability, respectively. Simulation results show that these modifications yield significant performance gains.     

Two-Dimensional Equalization/Detection for Patterned Media Storage

Although patterned media storage (PMS) is a promising candidate for ultrahigh-capacity magnetic data storage, as the capacity of PMS increases, the bit error rate (BER) performance of the system is degraded by increased two-dimensional intersymbol interference (2D-ISI), which results from intertrack interference (ITI), intersymbol interference (ISI), and noise. To improve the system performance under these adverse effects and to increase the capacity, in this paper, we propose to use and/or devise two-dimensional equalization/detection techniques: iterative decision feedback detection (IDFD) and two-dimensional generalized partial response equalization, which is optimized in minimum mean square error (MMSE), followed by one-dimensional Viterbi algorithm (2D-GPR/1D-VA). We evaluate the performance of the proposed methods by using numerical experiments under different amounts of 2D-ISI and noise. Simulation results suggest that under high storage density, the performance of the IDFD is improved by using more iterations and that under the same computational load, 2D-GPR/1D-VA performs better than IDFD. 2D-GPR/1D-VA, therefore, is a good candidate for ultrahigh-capacity PMS.     

基于硬判决的BICM-ID系统的迭代载波相位同步算法

在基于迭代译码的比特交织编码调制(BICM-ID)系统中,提出了一种利用译码产生的硬判决信息作引导的迭代载波相位同步的算法.该算法等效于期望最大化(EM)算法,收敛于最大似然(ML)估计,利用Viterbi译码过程中产生的数据比特硬判决信息,通过迭代地在同步和解码之间交换信息来完成联合解码和载波相位同步,实现了联合解码同步.仿真结果验证了该算法的有效性.在相偏θ∈[-20°,20°]时,其误码率性能最佳;在迭代次数达到5次时,误码率性能基本接近理想同步性能,较传统的相位同步算法具有更优的性能.随着信噪比的增大,能更快地逼近理想同步性能.     

Noise reduction using mean shift algorithm for estimating 3D shape

Abstract

The technique to estimate the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus — sharpness — is the crucial part for final 3D shape estimation. However, it is difficult to compute accurate and precise focus value because of the noise presence during the image acquisition by imaging system. Various noise filters can be employed to tackle this problem, but they also remove the sharpness information in addition to the noise. In this paper, we propose a method based on mean shift algorithm to remove noise introduced by the imaging process while minimising loss of edges. We test the algorithm in the presence of Gaussian noise and impulse noise. Experimental results show that the proposed algorithm based on the mean shift algorithm provides better results than the traditional focus measures in the presence of the above mentioned two types of noise.     

基于FxKalman算法的有源控制器设计与实现研究

近年来,对于有源噪声控制算法的性能越来越重视。与基于维纳滤波原理的最小均方滤波(Filtered-x Least Mean Square,Fx LMS)、最小二乘滤波(Filtered-x Recursive Least Square,Fx RLS)算法相比较,基于卡尔曼滤波的有源控制算法(Filtered-x Kalman,Fx Kalman)具有较快的收敛速度和良好的跟踪性能,且对带宽噪声有较好的降噪性能。设计、仿真运行了Fx Kalman算法的有源控制器,并针对单频、窄带和宽带信号,在实验室封闭空间对Fx Kalman算法、Fx LMS算法和Fx RLS算法进行有源控制器验证性实验比较,证实了Fx Kalman有源控制器具有上述优点。而如果初级噪声为单频信号且对算法收敛速度要求不高,Fx LMS算法是最经济稳妥的选择。当需要控制带宽噪声或对算法收敛速度要求较高时,Fx Kalman算法则为最好的选择。     

A study of PRML systems by write and read equalization

A new partial response (PR) channel which contains the write equalizer in addition to conventional PR channel is proposed. Our write equalizer outputs DC-free waveforms of two levels. Our read equalizer can reduce high-frequency noise, and what is more, it has little degradation of eye opening. The PR channels in combination with Viterbi decoder give partial response maximum-likelihood (PRML) systems which exhibit a good performance. The bit error rates for several PRML systems are obtained by computer simulation, where a Lorentzian channel with additive white Gaussian noise is assumed. The results shows that PR(1,1,-1,-1) system has an excellent performance in high density recording for a system in which the small transition spacings required by our write equalization is allowed     

High-rate coding system for magnetic recording

A coding system for magnetic recording channels combining a high-rate error detection code with a list Viterbi algorithm (LVA) is proposed. This coding system can provide a coding gain of up to 2-3 dB for a modified extended extended partial response class IV (EEPR4) channel. A pipelined implementation of the LVA is presented, and an accurate estimation technique for the LVA performance is developed     

Detection for channels with transition noise

Transition noise is known to be a major cause of errors for high density magnetic recording. This noise is signal dependent and can be modeled as multiplicative noise in a linear channel model. The maximum-likelihood method was not considered for detection of signals in such noise in the past. In this study, a detector model for an asymptotic maximum-likelihood (AML) detection is developed for systems with such noise. Based on a linear partial response channel model, a recursive procedure is obtained as a tree search algorithm, leading to the maximum likelihood detection asymptotically, as the tree-search depth is increased. Performance estimation will be discussed in a separate paper     

Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.     

一种计算航船对近海海洋环境噪声贡献的算法

为了研究浅海中低频段的海洋环境噪声，文章构建了一种计算航船对近海海洋环境噪声贡献的算法，利用某海域航船信息，结合实际水文参数，对该海域的航运噪声进行了仿真计算。航船信息通过访问船舶自动识别系统数据库获取。主要关注50~400 Hz的中低频段，将仿真计算结果和实验数据进行对比，验证了算法的可靠性，并进行了误差分析。利用该算法可获取接收点处航船噪声的水平方向分布特点，并可初步定量分析航船噪声对海洋环境噪声的贡献。     

语音模糊消噪算法

针对加性有色噪声,提出了语音信号模糊消噪算法;建立并训练了一个语音模糊消噪系统——自适应神经模糊推理系统（ANFIS）;用其对含噪语音中的有色噪声进行模糊估计,从而提取出干净的语音。对算法进行了仿真实验,结果表明,对模拟有色噪声在-17dB时能提取出清晰的语音。     

一种基于累积量的ESPRIT型盲波束形成算法

讨论一种用于非高斯信号源方位估计的盲波束形成算法。假设背景噪声是二阶统计未知的有色(空间相关)高斯噪声,基于“阵元对”模型的ESPRIT方位估计算法可以通过用累积量矩阵取代自相关矩阵完成信号空间的重建。经过这种处理,加性有色高斯噪声可被滤除,因此算法不需要相关噪声矩阵的知识。当加性噪声源是空间相关矩阵未知的有色高斯噪声时,计算机仿真比较了基于累积量的ESPRIT算法与基于二阶统计的ESPRIT算法的性能。     

基于小波变换的MR图像的去噪处理

根据小波变换多尺度下图像信号与噪声不同性质的理论,提出了一种新的MR图像去噪处理 算法,这种算法直接比较相邻小波域的系数,根据比较结果设立滤波器去除噪声。计算机模拟结果表明:这种算法的速度快,能去除渗噪图91%的噪声,图像的信噪比从3.1861提高到了13.4784。     

三类有源噪声控制算法性能比较

设计了FxLMS、GSFxAP、FsLMS等三类有源控制算法的仿真实验和消声室实验，分析了算法性能，对算法的适用条件进行深入研究。结果表明：当次级通路为线性通路时，在实际应用对收敛速度要求不高的情况下，选择FxLMS算法的性能代价比最高。如果欲控制的噪声为非平稳噪声或对算法收敛速度要求较高，GSFxAP算法是最优选择。如果参考信号与初级信号相关性差，选用FsLMS算法最为合适。上述结论为实际工程中有源控制算法的选择提供了理论依据。