Wiener Inverse Filter Research Articles

Fast algorithm for zero‐phase linear filter using discrete cosine transform

This Letter deals with a practical signal processing problem where the filter is zero-phase and is specified by the frequency response. The authors show that if the signal is symmetrically extended, then the fast Fourier transform (FFT)-based algorithm can be efficiently implemented by using the discrete cosine transform without signal extension. They extend the proposed algorithm to 2D filters and show that computationally it is at least four times more efficient than the FFT-based algorithm. A practical application of the new algorithm is a fast Wiener inverse filter, which is used to estimate images taken under the condition of air turbulence.

A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 μm wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper’s findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded.

Magneto-acousto-electrical Measurement Based Electrical Conductivity Reconstruction for Tissues.

Based on the interaction of ultrasonic excitation and magnetoelectrical induction, magneto-acousto-electrical (MAE) technology was demonstrated to have the capability of differentiating conductivity variations along the acoustic transmission. By applying the characteristics of the MAE voltage, a simplified algorithm of MAE measurement based conductivity reconstruction was developed. With the analyses of acoustic vibration, ultrasound propagation, Hall effect, and magnetoelectrical induction, theoretical and experimental studies of MAE measurement and conductivity reconstruction were performed. The formula of MAE voltage was derived and simplified for the transducer with strong directivity. MAE voltage was simulated for a three-layer gel phantom and the conductivity distribution was reconstructed using the modified Wiener inverse filter and Hilbert transform, which was also verified by experimental measurements. The experimental results are basically consistent with the simulations, and demonstrate that the wave packets of MAE voltage are generated at tissue interfaces with the amplitudes and vibration polarities representing the values and directions of conductivity variations. With the proposed algorithm, the amplitude and polarity of conductivity gradient can be restored and the conductivity distribution can also be reconstructed accurately. The favorable results demonstrate the feasibility of accurate conductivity reconstruction with improved spatial resolution using MAE measurement for tissues with conductivity variations, especially suitable for nondispersive tissues with abrupt conductivity changes. This study demonstrates that the MAE measurement based conductivity reconstruction algorithm can be applied as a new strategy for nondestructive real-time monitoring of conductivity variations in biomedical engineering.

Unsupervised spatial-resolution enhancement of electron beam measurement using deconvolution

This paper presents an unsupervised de-blurring technique that enhances the spatial resolution of electron beam (EB) diagnostic instruments. The inherent degradation in EB diagnosis waveforms is modeled using the convolution between the EB current distribution and the sensor characteristic function. Due to the use of various sensors, the diagnosis results are device dependent. Sensor inaccuracy has a detrimental effect on the deconvolution-based restoration of the ground truth signal. In this research this adverse effect is mitigated by an accurate sensor's Point Spread Function (PSF) derivation, which allows the successful restoration of waveforms. As approximate size of the sensor is known, a probability distribution is assigned to the expected interval of PSF features in the frequency domain, which increases the accuracy of the PSF analysis. By deploying this method, sensor inaccuracies are considered in the dynamic PSF formation, hence providing a promising consistency to the restoration. Restoration is performed with Wiener inverse filter and blind-deconvolution and results are compared with the ground truth pulses, obtained using a sensitive sensor (18um diameter). Experimental results confirm that the purposed method delivers a universal device independent measurement, facilitates instrument production, and EB characterization.

Reconstructing outside pass-band data to improve time resolution in ultrasonic detection

A combination deconvolution method is presented to increase the time resolution of an ultrasonic echo signal by reconstructing outside pass-band data. By the modified traditional Wiener inverse filter the filtered data are estimated, an autoregression (AR) model is obtained for these data inside the pass bands. Only the high frequency side data out of the pass bands are renewed by the autoregressive spectral extrapolation method. The optimized estimation is reconstructed using the combination of renewed and filtered data inside the pass bands. The results from the computer simulation and the experiments on measuring the thickness of thin multilayer materials show that this approach has improved time resolution performance.

Micro-assembly Robot System

A micro-assembly robot system with multi-manipulator cooperation is developed.The system includes two master micromanipulators with 4 DOFs and one slave micromanipulator with 3 DOFs and adopts double optical path orthogonal micro-vision as the main means to obtain the poses and environmental information of robot and micro-objects.To adapt to the operation requirements of micro-objects of different shapes and materials,the vacuum micro-gripper and piezoelectric ceramic micro-gripper are designed respectively,and their control methods are given on the basis of analyzing the working principle of micro-grippers.Problems such as micro-image acquisition and visual servo are studied,including wiener inverse filter restoration of blurred image,auto focus of microscope based on image analysis,and micro-vision servo control based on the image features of distance and angle.A semi-autonomous control scheme with human-robot interaction is utilized in the micro assembly operation.Assembly results demonstrate that the system works reliably and can accomplish micro assembly task that has complicated technological requirements.Presently the smallest assembly accuracy of micro-parts can attain 30 μm.

Resolution and signal processing technique of surface charge density measurement with electrostatic probe

When an electrostatic probe is used to measure the surface charge on an insulating plate of constant thickness, the measuring system is regarded a shift-invariant system and the relation between the surface charge density and the probe output can be treated in the spatial frequency domain. The distribution of the surface charge density on an insulating plate just after occurrence of a surface discharge is measured by a Pockels probe, which is regarded as a kind of electrostatic probe without the guard electrode, and restored by Wiener inverse filter. The performance of a Pockels probe and a conventional electrostatic probe are compared quantitatively in terms of the spatial resolution. In the case that the measured object is 3 mm thickness PMMA plate and is charged up to 10 nC/cm/sup 2/ in atmospheric air, it is estimated that the spatial resolution of the Pockels probe with 0.2 mm gap is 1.5 mm and that of the conventional electrostatic probe with the grounded guard electrode with 3 mm gap is 2.2 mm.

The practical significance of two-dimensional deconvolution in echography

This paper evaluates deconvolution (inverse filtering) as applied to ultrasonic imaging systems, and discusses the obstacles which are encountered employing the technique in practice. A minicomputer is used to generate artifical echo signals, simulating rf signals resulting from a set of point reflectors in a homogeneous medium, as recorded by an electronically focused group-steered linear array scanner. Two-dimensional deconvolution in combination with a Wiener noise reduction filter (i.e., a Wiener-Inverse filter) is applied to these simulated rf signals, which were contaminated with white noise. The efficacy of the Wiener-Inverse filter is defined in terms of its ability to resolve two point reflectors with a lateral spacing equal to the local −6 dB width of the ultrasonic beam. In favorable circumstances, the targets are resolved at signal-to-noise ratios (SNR) better than 20 dB, where SNR is defined as the maximum signal power divided by the average noise power level. Nonlinear effects due to quantization or signal clipping are investigated. In order to improve the resolution of an rf signal with a dynamic range of 40 dB, the input signal should be digitized at a minimum of 12 bits. The problem of signal clipping can be circumvented by oversampling. The two-dimensional Wiener-Inverse filter is defined in terms of both temporal and spatial properties of the insonification. Effects of wave diffraction give rise to a depth-dependent ultrasonic beam. As a result of a misfit of the Wiener-Inverse filter and the local properties of the ultrasonic beam, erroneous noisy texture arises in the image. Adaptation of the Wiener-Inverse filter with respect to the beam properties gives acceptable results, at the expense of a rather large computational effort.

The practical significance of two-dimensional deconvolution in echography.

This paper evaluates deconvolution (inverse filtering) as applied to ultrasonic imaging systems, and discusses the obstacles which are encountered employing the technique in practice. A minicomputer is used to generate artificial echo signals, simulating rf signals resulting from a set of point reflectors in a homogeneous medium, as recorded by an electronically focused group-steered linear array scanner. Two-dimensional deconvolution in combination with a Wiener noise reduction filter (i.e., a Wiener-Inverse filter) is applied to these simulated rf signals, which were contaminated with white noise. The efficacy of the Wiener-Inverse filter is defined in terms of its ability to resolve two point reflectors with a lateral spacing equal to the local -6 dB width of the ultrasonic beam. In favorable circumstances, the targets are resolved at signal-to-noise ratios (SNR) better than 20 dB, where SNR is defined as the maximum signal power divided by the average noise power level. Nonlinear effects due to quantization or signal clipping are investigated. In order to improve the resolution of an rf signal with a dynamic range of 40 dB, the input signal should be digitized at a minimum of 12 bits. The problem of signal clipping can be circumvented by oversampling. The two-dimensional Wiener-Inverse filter is defined in terms of both temporal and spatial properties of the insonification. Effects of wave diffraction give rise to a depth-dependent ultrasonic beam. As a result of a misfit of the Wiener-Inverse filter and the local properties of the ultrasonic beam, erroneous noisy texture arises in the image. Adaptation of the Wiener-Inverse filter with respect to the beam properties gives acceptable results, at the expense of a rather large computational effort.

Resolution performance of Wiener filters

To improve the resolution of seismic events, one often designs a Wiener inverse filter that optimally (in the least‐squares sense) transforms a measured source signature into a spike. When this filter is applied to seismic data, the bandwidth of any noise which is present increases along with the bandwidth of the signal. Thus the signal‐to‐noise ratio is degraded. To reduce signal ambiguity it is common practice to prewhiten the Wiener filter. Prewhitening the filter improves the output signal‐to‐ambient noise ratio, but at the same time it reduces resolution. The ability to resolve the temporal separation between events is determined by the resolution time constant which we define as the ratio of signal energy to peak signal power from the filter. For unfiltered wavelets the resolution time constant becomes the reciprocal of resolving power recently described by Widess (1982). For matched filter signals the resolution time constant can be regarded as the inverse of the frequency span of the signal. Although it is satisfying that the resolution time constant definition agrees with other measures of resolution, this more general definition has two major advantages. First, it incorporates the effect of filtering; second, it is easily generalized to incorporate the effects of noise by assuming that the filter is a Wiener filter. For a given amount of noise the Wiener filter is a generalization of the matched filter. Marine seismic wavelets demonstrate how reducing the noise level improves the resolution of a Wiener filter relative to a matched filter. For these wavelets a point of diminishing return is reached, such that, to realize a further small increase in resolution, a large increase in input signal‐to‐noise ratio is required to maintain interpretable information at the output.