Wiener Filtering In Wavelet Domain Research Articles

Application of Image Recognition Algorithm Based on Partial Differential Equation and Wavelet Transform in the Intelligent Linkage System of Government Urban Public Management

In the Internet age, social needs have become more diverse. The private sector has achieved great success in integrating resources to provide services to customers thanks to technological breakthroughs such as mobile Internet and big data, while the public sector has become even more dwarfed. The public sector urgently needs new theoretical support to integrate administrative resources more effectively, so as to provide the public with one-stop, holistic and seamless services as a whole. The “holistic governance” theory effectively responded to the abovementioned problems and gradually condensed the consensus of the academic and practical circles and became the most influential theory. The method of mathematical modeling is used to combine the improved wavelet domain Wiener filter and the comprehensive model of partial differential equations. This paper proposes an image recognition algorithm based on partial differential equations and wavelet transform. The algorithm uses weight coefficients to organically combine high-order partial differential equation models, retains the advantages of the second- and fourth-order partial differential recognition models, effectively improves the ability to maintain image edge detail information, and achieves better recognition results. This article starts with the description of the fragmentation of a street’s grassroots governance and sorts out the process of implementing overall governance with the “big linkage mechanism” as the main carrier. Due to the limitations of research ability and objective conditions, this article intercepts the “cross-section” of the big linkage mechanism in a certain street to demonstrate it and analyzes this “cross-section” more thoroughly. The experimental results of the data show that the image recognition algorithm based on partial differential equations and wavelet transform is effective and practical in the intelligent linkage system of the government urban public management.

The method to capture the form of opera performance based on machine vision

In order to overcome the problems of traditional opera performance form capture methods, such as long denoising time and low capture accuracy, this paper proposes a new opera performance form capture method based on machine vision. The wavelet domain and gradient domain are obtained by multi-step local Wiener filtering in wavelet domain. The parameters of wavelet-based Wiener filter are modified to obtain a new wavelet threshold function. The grey images of different components of different opera performance form images are extracted for comparison, and the best component image is selected for threshold processing according to different opera performance forms. At the same time, image features are extracted in the form of binary image to realise the opera performance form capture based on machine vision. Experimental results show that the proposed method can quickly and accurately capture opera performance form, and the highest capture efficiency can reach 99.55%.

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images.

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

Wiener filter-based wavelet domain denoising

The wavelet domain Wiener filter has been widely adopted as an effective image denoising method that has low complexity. In this paper we propose a novel Wiener filter with high-resolution estimation that determines the signal power while preserving the edge information. We assume that a noisy image is composed of noise and the original image, which are mutually orthogonal. Based on this assumption, we utilize the local covariance to obtain high-resolution coefficients from the low-resolution coefficients and to estimate the signal variance in the Wiener filter by using the high resolution values. The experimental results show that the proposed algorithm improves the objective and subjective performance significantly.

Wavelet‐based blind deconvolution of near‐field ultrasound scans

A wavelet-based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near-field ultrasound imaging for non-destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum-based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log-spectrum – estimate the pulse by exploiting the pulse log-spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log-spectrum is localised with respect to time as the continuous wavelet transform (CWT) log-scalogram to deal with the non-stationarity of the near-field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log-scalogram and is deconvolved by wavelet-domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans.

基于小波域的维纳滤波算法在锥束牙科CT中的应用

基于小波域的维纳滤波算法在锥束牙科CT中的应用

Comparative Study of Speckle Noise Reduction of Ultrasound B-scan Images in Matrix Laboratory Environment

ABSTRACT In the field of biomedical imaging, the ultrasound (US) B-Scan images are used for tissue characterization. These images are obtained with a simple linear or sector scan US probe, which show a granular appearance called speckle. Speckle is modeled as a signal dependent noise, which tends to reduce the image resolution and contrast, thereby reducing the diagnostic values of the US imaging modality. Over a period, various speckle reduction techniques have been developed by researchers did not represent a comprehensive method that takes all the constraints into consideration. This work addressed the Wiener filtering in wavelet domain with soft thresholding as a comprehensive technique. Also, this paper compares the efficiency of the wavelet-based thresholding (VisuShrink, BayesShrink and SureShrink) technique in despeckling the medical US images with five other classical speckle reduction filters. The performance of these filters are determined by the statistical quantity measures such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE).The results obtained are presented in the form of filtered images, statistical tables and diagrams. Based on the statistical measures and visual quality of the US B-scan images the Wiener filtering with BayesShrink thresholding technique in the wavelet-domian performed well over the other filter techniques.

A Stationary Wavelet-Domain Wiener Filter for Correlated Speckle

In this paper, we develop a Wiener-type speckle filter that operates in the stationary wavelet domain. We denote it as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stationary</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wavelet-domain</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wiener</i> (SWW) speckle filter. We assume that both the speckle-free image and the speckle contribution have spatial correlations and utilize well-established models for the power density spectrum of the radar cross section to estimate the autospectra that define the filter. It turns out that the filter is independent of the wavelet-domain scale level, i.e., the filter is the same at all scale levels. The SWW filter works on nonoverlapping blocks in the wavelet domain, which are obtained by a quadtree algorithm. Due to the dyadic support of the wavelet coefficients, a natural smoothing is carried out on the boundaries between neighboring blocks, and no visual boundary effects can be observed. The SWW filter is unbiased and shows good performance in despeckling synthetic aperture radar (SAR) images. It smooths homogeneous areas while preserving textured areas and point scatterers. In contrast to most other speckle filters, the SWW filter requires the SAR data to be given in single-look complex form.

Restoration of fMRI Signal using Wiener Filter in a Wavelet Domain

Wavelet-based denoising methods have the advantage over low-pass filtering in that relevant detail information is retained, while small details due to noise are discarded. This paper reports a novel technique of removing noise, using a wiener filter in wavelet domain, from an fMRI data and display selectively event related voxels in a spatial domain. After the fMRI signal is wavelet transformed to temporal domain, its median and mean become symmetric as described in Section and its median of absolute deviation (MAD) is calibrated with the standard deviation of a Gaussian distribution to approximately determine the standard deviation of the noise in the fMRI signal. Once the noise level is determined, the result is used to determine the power spectrum of the coherent signal and the corresponding standard variable. The whole process is used to accurately estimate the power of the coherent signal and the associated noise at a given voxel location. The estimated power spectrum is used to approximate the optimal wiener filter coefficient for removing the noise from the fMRI signal. In this experiment, only the signal to be restored is available and all prior knowledge about the ideal signal has to be estimated from it. Though an fMRI is contaminated by both Gaussian and Rician distribution related noises, it is safe to assume noise in an fMRI signal to be additive white Gaussian noise. In this paper, we showed that the power spectrum estimated from this single copy of degraded signal is a true power spectrum of the signal, and as a result, the restoration filter or noise removing filter is optimal, though there is a lack of accurate prior information. The method successfully removes noises and exposes activated voxels in fMRI signal all the time as shown in this paper.

Lifting-based wavelet domain adaptive Wiener filter for image enhancement

A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR.

Beat-to-beat noise removal in noninvasive His-bundle electrocardiogram

His-bundle electrocardiogram micropotentials are usually obtained by serial-signal averaging, because no acceptable, satisfactory solution for beat-by-beat removal of power-line interference and electromyographic noise has been found. A method has been developed for surface beat-to-beat His-bundle potential recovery, with the hypothesis that no distortion in the signal shape is admissible. It is based on consecutive power-line interference subtraction and wavelet-domain electromyographic noise suppression, modified to match the strict criteria for detecting low-amplitude His potentials. The beat-to-beat wavelet-domain Wiener filtering uses a pilot signal estimate obtained from a limited number (around 20) of heart beat averages. The method resulted in an improvement of more than 4.5 dB in the signal-to-noise ratio and more than 20% reduction in mean absolute error, both measured along the P-Q segment. It is applicable for ECG signals contaminated by moderate electromyogram noise, with an initial signal-to-noise ratio of 15 dB or higher.