Blind Deconvolution Method Based on Precondition Conjugate Gradients

In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.

A New Fast Iterative Blind Deconvolution Algorithm

Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size, which yields the optimum restored image quality for both noisy and noiseless images. It is based on evaluating the detail energy of the wave packet decomposition of the blurred image. The minimum detail energies occur at the optimum PSF size. Having accurately estimated the PSF, the paper also proposes a fast double updating algorithm for improving the quality of the restored image. This is achieved by the least squares minimization of a system of linear equations that minimizes some error functions derived from the blurred image. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of some of the detail wavelet packet energies. Simulation results of several examples have verified that the proposed technique manages to yield a sharper image with higher PSNR than classical approaches.

A novel blind deconvolution algorithm using single frequency bin

Former frequency-domain blind devolution algorithms need to consider a large number of frequency bins and recover the sources in different orders and with different amplitudes in each frequency bin,so they suffer from permutation and amplitude indeterminacy troubles. Based on sliding discrete Fourier transform,the presented deconvolution algorithm can directly recover time-domain sources from frequency-domain convolutive model using single frequency bin. It only needs to execute blind sepa-ration of instantaneous mixture once there are no permutation and amplitude indeterminacy troubles. Compared with former algorithms,the algorithm greatly reduces the computation cost as only one frequency bin is considered. Its good and robust per-formance is demonstrated by simulations when the signal-to-noise-ratio is high.

Multiframe Blind Super Resolution Imaging Based on Blind Deconvolution

As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.

Astronomical image restoration using variational Bayesian blind deconvolution

An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images.Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis,approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler(KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.

PERFORMANCE OF BLIND DECONVOLUTION IN OPTOACOUSTIC TOMOGRAPHY

In this paper,we consider the use of blind deconvolution for optoacoustic(photoacoustic)imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system.The method is demonstrated with optoacoustic measurement obtained from six-day-old mice,imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration.Wefind that estimates of the unknown point spread function,achieved by blind deconvolution,improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

Blind Deconvolution of Seismic Data Based on the Spearman’s Rho

In this paper, we propose a novel seismic blind deconvolution approach based on the Spearman’s rho in the case of band-limited seismic data with a low dominant frequency and short data records. The Spearman’s rho is a measure of the dependence between two continuous random variables without the influence of the marginal distributions, by which a new criterion for blind deconvolution is constructed. The optimization program for new criterion of blind deconvolution is performed by applying Neidell’s wavelet model to the inverse filter. The noise-free and noisy synthetic data, onshore seismic trace in the Ordos Basin, and offshore stacked section in the Bohai Bay Basin examples show good results of the method.

AN IMPROVED FAST BLIND DECONVOLUTION ALGORITHM BASED ON DECORRELATION AND BLOCK MATRIX

In order to alleviate the shortcomings of most blind deconvolution algorithms,this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix.Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms,it has a constraint that the number of the source signals must be less than that of the channels.The improved algorithm deletes this constraint by using decorrelation technique.Besides,the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix.Simulation results demonstrate the validation and fast separation of the improved algorithm.

ITERATIVE MULTICHANNEL BLIND DECONVOLUTION METHOD FOR TEMPORALLY COLORED SOURCES

An iterative separation approach, i.e. source signals are extracted and removed one by one, is proposed for multichannel blind deconvolution of colored signals. Each source signal is extracted in two stages: a filtered version of the source signal is first obtained by solving the generalized eigenvalue problem, which is then followed by a single channel blind deconvolution based on ensemble learning. Simulation demonstrates the capability of the approach to perform efficient mutichannel blind deconvolution.

A Modified Eigenvector Method for Blind Deconvolution of MIMO Systems Using the Matrix Pseudo-Inversion Lemma

Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.

Blind Deconvolution Processing of Loop Inductance Signals for Vehicle Reidentification

Vehicle reidentification is an elegant solution for gathering several pieces of valuable traffic information, e.g., space mean speed, travel time, vehicle tracking, and origin/destination data. Recently, a number of vehiclereidentification algorithms utilizing inductive loop signals have been proposed to take advantage of the widespread availability of loop detectors. These algorithms, however, all directly utilize the raw inductance signals for pattern matching and feature extraction without deconvolution. The raw loop signals are essentially a convolved output between the true vehicle inductance signature and the loop system function, and thus a deconvolution is needed in order to expose the detailed features of individual vehicles. The purpose of this paper is to present a recent investigation on restoration of true inductance signatures by applying a blind deconvolution process. The main advantage of blind deconvolution over the conventional deconvolution is that the computation does not require modeling of a precise loop-detector system function. Experimental results show that the proposed blind deconvolution reveals much more detailed features of inductance signals and, as a result, increases the vehicle reidentification accuracy.

Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation

In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the esti- mated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.

Inspection of the Output of a Convolution and Deconvolution Process from the Leading Digit Point of View—Benford’s Law

In the communication field, during transmission, a source signal undergoes a convolutive distortion between its symbols and the channel impulse response. This distortion is referred to as Intersymbol Interference (ISI) and can be reduced significantly by applying a blind adaptive deconvolution process (blind adaptive equalizer) on the distorted received symbols. But, since the entire blind deconvolution process is carried out with no training symbols and the channel’s coefficients are obviously unknown to the receiver, no actual indication can be given (via the mean square error (MSE) or ISI expression) during the deconvolution process whether the blind adaptive equalizer succeeded to remove the heavy ISI from the transmitted symbols or not. Up to now, the output of a convolution and deconvolution process was mainly investigated from the ISI point of view. In this paper, the output of a convolution and deconvolution process is inspected from the leading digit point of view. Simulation results indicate that for the 4PAM (Pulse Amplitude Modulation) and 16QAM (Quadrature Amplitude Modulation) input case, the number “1” is the leading digit at the output of a convolution and deconvolution process respectively as long as heavy ISI exists. However, this leading digit does not follow exactly Benford’s Law but follows approximately the leading digit (digit 1) of a Gaussian process for independent identically distributed input symbols and a channel with many coefficients.

针对冲击性故障信号的谱融合特征提取算法

利用盲解卷积方法在时域中进行故障信号特征提取时,常会出现多个信号混淆分离结果,但以往的研究中只强调了分离的部分,而很少对分离后的信号进行进一步的处理,给实际应用造成不便。这里在盲解卷积和谱融合的基础之上,使用核改进的模糊c均值聚类算法,针对机械故障信号的脉冲特性,提出一种针对冲击性故障信号处理的实用型算法。计算机仿真实验证实了该算法的有效性。此算法优化了以往的聚类筛选方法,可以有效排除反卷积后诸多无用信号的干扰,将故障脉冲信号的特征准确提取出来,能提高故障诊断的效率。

基于最大平均峭度盲解卷积的直升机故障诊断

针对最小熵解卷积(minimum entropy deconvolution,简称MED)应用于故障诊断时倾向于恢复少量主导冲击而非周期性故障冲击的问题,定义一种滤波器系数求解指标——平均峭度,提出了最大平均峭度盲解卷积方法。首先,通过对故障信号进行均等分割,取各分割段信号峭度的均值,得到信号的平均峭度;其次,将平均峭度作为信号盲解卷积指标,求解滤波器系数;最后,完成信号滤波,提取周期性故障冲击。仿真信号与直升机故障诊断案例分析结果表明:所提最大平均峭度盲解卷积方法能从含复杂干扰成分的故障信号中恢复故障冲击序列,为故障诊断提供可靠信息;相比于MED等传统盲解卷积方法,所提方法具有较强的普适性。

利用盲反卷积和混沌振子增强船舶辐射噪声解调线谱

针对经典解调方法中心频率、带宽选择困难和解调线谱受带外噪声干扰难以分辨等问题,提出采用盲反卷积和混沌振子方法抑制带外噪声,增强船舶噪声解调线谱。该方法通过Duffing振子预检宽带船舶噪声低频弱周期信号,随后将相应频率作为最小噪声幅值比反卷积(MNAD)方法的先验参数,利用MNAD方法自适应搜索解调频带得到可清晰分辨的高信噪比解调线谱。仿真和实测数据分析表明,该方法较经典解调方法和其他盲反卷积方法,可获取更佳的中心频率和滤波带宽,所得解调线谱的窄带信噪比DF值最高。

改进融合指标的新型盲解卷积算法在轴承故障诊断中的应用

为解决现有盲解卷积算法易受随机脉冲影响的问题,综合时域特征和频域特征,提出一个新的故障敏感指标,即包络谱峭度-包络基尼系数融合指标(Envelope Spectral Kurtosis-envelope Gini Index,ESKEG)。该指标对周期性脉冲更敏感,不易受随机脉冲的影响。基于该指标,提出一个新的解卷积算法,即基于最大ESKEG的盲解卷积,并采用粒子群算法(Particle Swarm Optimization,PSO)求解滤波器系数。通过仿真振动信号和实验仿真信号进行验证,结果表明相比于其他盲解卷积算法,所提出的PSO-ESKEG算法在故障先验知识未知的情况下,能更有效避免受到随机脉冲信号的影响。

编码器时间序列重构和CYCBD在滚动轴承故障特征提取中的应用

针对最大二阶循环平稳盲解卷积(CYCBD)算法在轴承故障特征提取中的有效性及计算效率受滤波器长度影响的问题,提出谐波谱峰因子(HSC)作为评价指标自适应确定CYCBD的滤波器长度,通过编码器时间序列重构的方法平衡优化过程的计算效率。根据滚动轴承固有参数计算轴承故障阶次,并根据其设置循环频率;根据故障阶次确定时间序列重构的脉冲数;用中心差分法计算重构后信号的瞬时角速度;采用等步长搜索策略以谐波谱峰因子作为评价指标自适应确定CYCBD的滤波器长度;根据谐波谱峰因子最大时对应的阶次谱揭示滚动轴承故障特征。仿真和试验数据分析结果表明,所提方法能自适应确定滤波器长度,对提高CYCBD算法计算效率有明显效果,适用于滚动轴承故障特征提取。

无人机视角下的红外图像去模糊算法

针对油气长输管道采用无人机巡检时所拍摄的红外图像去模糊问题,利用图像通道的先验知识提升模糊图像质量,分别基于双边滤波和非盲去模糊网络NBDN去除人工伪影的方法达到更佳的图像复原效果。首先,基于暗通道先验知识,在最大后验的优化框架中添加暗通道的L_(0)正则项;然后使用图像梯度的L_(0)正则项,代替图像像素的L_(0)正则项作为潜在图像的正则化约束,使用迭代交替估计图像模糊核和中间潜在图像;采用半二次分裂方法和查表法间接优化求解,估计中间潜在图像;采用双线性插值估计图像模糊核,通过对图像进行上下采样,构建图像金字塔,进而利用共轭梯度法直接优化求解。最后,利用估计的模糊核,使用基于超拉普拉斯先验的图像非盲去模糊方法得到潜在图像I_(1);使用基于L_(0)正则化的非盲去模糊方法得到潜在图像I_(0);计算估计的潜在图像I_(1)和I_(0)之间的差值映射,从I_(1)中减去双边滤波过滤后的差分图,得到最终的潜在图像I。将本文算法在低照度图像、含有饱和像素的图像、真实图像以及红外摄像图等图像数据上进行实验,相对于其他图像去模糊算法。实验结果表明:所提出的方法在多种模糊图像复原效果上均具有较强的竞争力。

基于参数自适应的RSSD-CYCBD及在轴承外圈故障特征提取中的应用

针对滚动轴承工作环境复杂、故障特征信号易被高强度噪声掩盖的问题,提出了基于参数自适应的共振稀疏分解(RSSD)和最大二阶循环平稳盲解卷积(CYCBD)的滚动轴承故障诊断方法。首先,利用人工大猩猩部队优化算法(GTO),结合相关系数与相关峭度的融合指标,自适应选择RSSD分解参数,得到了仿真信号的最优低共振分量;然后,利用GTO结合包络熵,自适应选择CYCBD的循环频率和滤波器长度,对最优低共振分量进行了解卷积运算,从包络谱中获得了信号的故障特征频率;最后,利用美国凯斯西储大学试验台和MFS-MG机械故障综合模拟试验台数据,综合验证了该方法的有效性,并将试验结果与RSSD-MCKD方法的结果进行了对比。研究结果表明,该方法能够准确地得到仿真信号的故障频率为20 Hz、美国凯斯西储大学试验台近似故障频率为107.5 Hz、MFS-MG试验台近似故障频率为87.6 Hz。自适应RSSD-CYCBD方法能够有效地识别出故障特征频率及其倍频,实现滚动轴承故障诊断的目的。