基于光谱采样率的反卷积算法分析

反卷积方法是提高光谱仪分辨率的重要手段。采用空域迭代反卷积和频域维纳滤波对多纵模激光器光谱进行数据仿真,并在不同光谱仪采样率条件下,比较了迭代反卷积和维纳滤波结果。仿真结果表明,迭代反卷积和维纳滤波可以有效消除光谱仪仪器响应函数引起的光谱展宽,提高光谱仪分辨率。在光谱仪采样率低的情况下,迭代反卷积的分辨率增强效果优于维纳滤波。随着采样率的增加,维纳滤波的误差小于迭代反卷积。实验分别测量了单纵模和多纵模632.8nm He-Ne激光器光谱,并对测量结果进行反卷积处理。结果表明,低分辨率光谱仪测量的激光器光谱经反卷积处理后与高分辨率光谱仪直接测量结果一致。     

基于迭代滤波盲复原算法的水下图像噪声去除

摘要：图像复原的目的是从观测到的退化图像重建原始图像,维纳滤波与约束去卷积滤波是比较常采用的复原方法。在未知降质函数的情况下,直接运用维纳滤波和约束去卷积滤波有一定困难。针对此提出以维纳滤波与约束去卷积滤波为模型的迭代滤波盲复原算法对水下图像进行去噪。实验证明,该方法获得了比较理想的复原效果。     

多传感器最优信息融合白噪声反卷积滤波器

基于Kalman滤波方法和白噪声估计理论,在线性最小方差按矩阵加权最优信息融合准则下,提出了带相关噪声系统多传感器信息融合白噪声反卷积滤波器.提出了各传感器滤波误差之间的协方差阵计算公式,可用于计算最优融合加权阵.同单传感器情形相比,可提高融合滤波精度.它可减少在线计算负担,便于实时应用.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernoulli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.     

De-noising techniques for terahertz responses of biological samples

Signal processing techniques may be used to improve the speed, resolution and noise robustness of pulsed terahertz (T-ray) imaging systems. Such systems have a wide range of applications and much recent interest has focussed on several promising biomedical fields. There are a number of significant challenges to be overcome before a commercial biomedical terahertz system can be realised. Recent research is focussed on the implementation of a high speed, compact and portable T-ray imaging system. This system will draw heavily on MOEMS technology. One of the major stages in the development of such a system is the design of efficient software algorithms to perform signal recognition and imaging operations in real time.

This paper considers a number of signal processing techniques suitable for de-noising and extracting information from the data obtained in a terahertz pulse imaging system. Two main de-noising techniques are considered. Wavelet de-noising and Wiener deconvolution algorithms are applied to the terahertz responses of biological samples including Spanish Serrano ham and an oak leaf.     

Reduction of noise in AVHRR channel 3 data with minimum distortion

The channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of weather satellites (NOAA 6-12) are contaminated by instrumentation noise. The signal to noise ratio (S/N) varies considerably from image to image and the between sensor variation in S/N can be large. The characteristics of the channel noise in the image data are examined using Fourier techniques. A Wiener filtering technique is developed to reduce the noise in the channel 3 image data. The noise and signal power spectra for the Wiener filter are estimated from the channel 3 and channel 4 AVHRR data in a manner which makes the filter adaptive to observed variations in the noise power spectra. Thus, the degree of filtering is dependent upon the level of noise in the original data and the filter is adaptive to variations in noise characteristics. Use of the filtered data to improve image segmentation, labeling in cloud screening algorithms for AVHRR data, and multichannel sea surface temperature (MCSST) estimates is demonstrated. Examples also show that the method can be used with success in land applications. The Wiener filtering model is compared with alternate filtering methods and is shown to be superior in all applications tested     

Evaluation and applications of the iterated window maximizationmethod for sparse deconvolution

Estimating a sparse signal from a linearly degraded and noisy data record is often desirable in seismic and ultrasonic applications. Bernoulli-Gaussian modeling and maximum a posteriori estimation has proven successful but entails computationally difficult optimization problems that must be solved by suboptimal methods. The iterated window maximization (IWM) algorithm was proposed for such optimization by Kaaresen (see ibid., vol.45, p.1173-83, 1997). The purpose of this paper is twofold. First, the IWM is evaluated against several established alternatives for Bernoulli-Gaussian deconvolution. The restoration quality is quantified by various loss functions, and the average performance is studied through simulation. In all cases examined, the IWM combined better restoration and significantly faster execution than the other algorithms. This motivates extension of IWM to other models, which is the second objective of this paper. Promising real data results are obtained from such diverse applications as robust modeling of ultrasonic nondestructive evaluation data, deblurring of two-dimensional (2-D) astronomical star fields, and segmentation of seismic well logs. It is also argued that IWM can be used for other deconvolution problems as long as the function to be reconstructed is, in some sense, sparse     

Wiener filter design using polynomial equations

A simplified way of deriving realizable and explicit Wiener filters is presented. Discrete-time problems are discussed in a polynomial equation framework. Optimal filters, predictors, and smoothers are calculated by means of spectral factorizations and linear polynomial equations. A tool for obtaining these equations, for a given problem structure, is described. It is based on the evaluation of orthogonality in the frequency domain, by means of canceling stable poles with zeros. Comparisons are made to previously known derivation methodologies such as completing the squares for the polynomial systems approach and the classical Wiener solution. The simplicity of the proposed derivation method is particularly evident in multistage filtering problems. To illustrate, two examples are discussed: a filtering and a generalized deconvolution problem. A new solvability condition for linear polynomial equation appearing in scalar problems is also presented     

多尺度时变地震信号反褶积

本文针对石油地震勘探中地震信号的时变性，提出一个新的时变地震信号反褶积方法。在分析地层时变滤波特性的基础上，构造多尺度地震子波。分别用每一个尺度的地震子波作确定反褶积，并用基于信息熵准则构造的检测函数综合多尺度反褶积结果。实验表明本文提出的方法具有很好的效果。     

The application of blind channel identification techniques toprestack seismic deconvolution

One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing     

Ultrasonic images interpretation improvement for microassembling technologies characterisation

This paper deals with two techniques for improvement of ultrasonic images interpretation based on image and digital signal processing. The first technique concerns image processing. We propose an analytical model to evaluate the effective Point Spread Function (PSF) of a reflection scanning ultrasonic system. We present the great interest of the knowledge of the PSF for deconvolution of ultrasonic images based on Lucy-Richardson algorithm. With this technique, lateral resolution is strongly improved and real dimensions of an object can be reached that is necessary to improve the understanding and interpretation of an image. The second technique concerns ultrasonic images showing different nature of defects. A better understanding of these images can be realized by means of time of flight detection. This technique concerns Time-Frequency (TF) analysis to determine nature and depth of defects with accuracy. Two applications of microassembling die-attach characterisation are presented using these methods.     

基于两维解卷积和稀疏回波去噪的高分辨雷达成像方法

该文提出了一种结合稀疏低秩矩阵恢复技术以及基于匹配滤波结果的反卷积算法的高分辨率雷达成像方法。对雷达回波信号进行匹配滤波操作可以最大化回波信噪比,通过推导发现经过匹配滤波操作后的回波信号可以建模为两维卷积的形式,对该结果做维纳滤波解卷积可以获得较高的分辨率。然而典型的解卷积算法面临着病态性问题,该问题会放大解卷积后的噪声、限制解卷积后的成像分辨率。文中证明了在目标稀疏分布的先验下,经过匹配滤波后的回波矩阵满足稀疏低秩的特性。在这种情况下,利用回波矩阵的稀疏低秩矩阵特征可以进一步提高信噪比,以减轻解卷积的病态性问题以及点扩散函数的平滑卷积造成目标散射低分辨率的影响。仿真实验以及实测数据证明了所提方法的有效性。      

Linear and nonlinear techniques for the deconvolution of hormonetime-series

Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. Here, the deconvolution of hormone time series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time series as well as on series of luteinizing hormone     

Optimal seismic deconvolution: distributed algorithms

Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelet's replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed solution. The new distributed methodology, perfectly suitable for a multisensor environment, such as the seismic signal processing, is compared to the centralized approach, with respect to computational complexity and architectural efficiency. It is shown that the distributed approach greatly outperforms the currently used centralized methodology offering flexibility in the design of the data fusion network     

UAV remote sensing atmospheric degradation image restoration based on multiple scattering APSF estimation

Unmanned aerial vehicle (UAV) remote imaging is affected by the bad weather, and the obtained images have the disadvantages of low contrast, complex texture and blurring. In this paper, we propose a blind deconvolution model based on multiple scattering atmosphere point spread function (APSF) estimation to recovery the remote sensing image. According to Narasimhan analytical theory, a new multiple scattering restoration model is established based on the improved dichromatic model. Then using the L0 norm sparse priors of gradient and dark channel to estimate APSF blur kernel, the fast Fourier transform is used to recover the original clear image by Wiener filtering. By comparing with other state-of-the-art methods, the proposed method can correctly estimate blur kernel, effectively remove the atmospheric degradation phenomena, preserve image detail information and increase the quality evaluation indexes.     

双线圈感应测井信号反褶积处理方法

在对于双线圈感应测井信号处理的研究中,提出两种对于几何因子信号反褶积处理的方法。包括了最佳维纳滤波反褶积与窗函数滤波反褶积对于背景电导率信号还原的处理方法。其特点在通过仿真,比较两种处理方法的有效性和各自的优势,有利于对于测量信号还原真电导率的实际应用,提高测量分辨率,使测量数据处理结果比传统处理更有利于薄层解析。     

Deconvolution and Weiner filtering of short-range radiometric images

A radiometric probe positioned at different points on the surface of a lossy medium can detect the presence of a sub-surface thermal noise source. Radiometric images are obtained by a combination of measured data and calculated weighting functions of the probe associated with the underlying lossy materials. The authors show that the quality of these images and their spatial resolution can be greatly improved by Wiener filtering deconvolution of their corresponding data.<>     

Multiple reflection attenuation in seismic data usingbackpropagation

Multiple reflections in seismic data are generally considered to be unwanted noise that often seriously impedes correct mapping of the subsurface geology in search of oil and gas reservoirs. We train a backpropagation neural network in order to recognize and remove these multiple reflections and thereby bring out the primary reflections underneath. The training data consist of model data containing all multiples and the corresponding seismic sections containing only the primary arrivals. The basis for the modeling is data from a real well log that is typical for the area in which the data were gathered. In contrast to existing conventional deconvolution methods, the neural network does not depend on such restricting assumptions concerning the underlying model as, for example, the Wiener filter, and it has the potential to be successful in cases where other methods fail. A further advantage of the neural net approach is that it is possible to make extensive use of a priori knowledge about the geology, which is present in the form of well log data. Tests with realistic data show the ability of the neural network to extract the desired information     

Radio-astronomical imaging in the presence of strong radiointerference

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio-astronomical imaging. Current deconvolution procedures, such as CLEAN, are shown to be unsuitable for spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources. This not only leads to an extended CLEAN algorithm, but also the formulation allows the insertion of other array signal processing techniques for direction finding and gives estimates of the expected image quality and the amount of interference suppression that can be achieved. Finally, a maximum-likelihood (ML) procedure for the imaging is derived, and an approximate ML image formation technique is proposed to overcome the computational burden involved. Some of the effects of the new algorithms are shown in simulated images     

Iterative image restoration using approximate inversepreconditioning

Removing a linear shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least-squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Theoretical results are established to show that fast convergence can be expected, and test results are reported for a ground-based astronomical imaging problem