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Signal Processing Techniques
Published in Petr Vaníček, Nikolaos T. Christou, GEOID and Its GEOPHYSICAL INTERPRETATIONS, 2020
The disadvantages of the Kalman filtering approach in general are that it must assume particular stochastic models for the geoid correlation structure and for the noise process (noise includes the errors in all correction terms plus all non-geoid components of the measurements — instrument noise, oceanographic noise, errors in tide corrections, etc.). Unmodeled errors in the estimated geoid heights and deflections will arise to the extent that the actual data diverge from these assumptions. When the assumed models are correct, the Kalman smoothing approach is the optimal approach. Note that the Wiener filter also requires assumptions about the power spectrum of the signal and the noise. The classical approaches are simpler than the Kalman or Wiener filter approaches, achieving their simplicity by incorporating only the bare minimum description of the signal and noise: they assume a high-frequency cutoff beyond which the noise dominates. The form of the low-pass filter chosen to “remove the noise” is generally whatever happens to be most easily available to the investigator, and the cutoff frequency is not chosen optimally.
Computer-Aided Detection of Breast Cancer on Mammograms
Published in J. Dinesh Peter, Steven Lawrence Fernandes, Carlos Eduardo Thomaz, Advances in Computerized Analysis in Clinical and Medical Imaging, 2019
M. Jayesh George, S. Perumal Sankar
Wiener filter is considered as the best method to reduce the general mean square error during smoothing of noise and inverse filtering. Wiener filter provides a linear prediction of actual input picture and least mean square error. The main aim of this filtering technique is to find a statistical estimate of unknown signal which will utilize an associated signal as a reference and filtering that signal to figure out the estimate of the final result. Wiener filter is mainly used to remove additive noise, which has constant spectral density, and reverse the obscuring simultaneously. Wiener filtering is superior to other methods in removing all visual artifacts. This filter gives the accurate technique for reducing the unarranged areas, with a precise aim to get the most suitable renewing of the original signal. [14] With reference to the psycho-visual criterion, most of the X-ray readers affirmed that Wiener filtering doesn’t alter the information content of mammogram images, but it gives superior visibility. [15]
Signal and Feature Compensation Methods for Robust Speech Recognition
Published in Gillian M. Davis, Noise Reduction in Speech Applications, 2018
Rita Singh, Richard M. Stern, Bhiksha Raj
The Wiener filter is a linear filter with an impulse response that is designed to minimize the expected squared error between the clean speech signal and the filtered noisy speech signal.8 It can be shown that in the most generic case the frequency response of the optimal filter is given by () H(ω)=Sxy(ω)Sy(ω)
Surgical phase recognition in laparoscopic videos using gated capsule autoencoder model
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Praveen SR Konduri, G Siva Nageswara Rao
The images collected from the dataset contain several noises, which may lead to higher computational complexity. Thus, the pre-processing stage is necessary to enhance image quality. The proposed study adopts Wiener and fast bilateral filtering to pre-process the input video frames. The purpose of the Wiener filter is to preserve the edges of the input image, and fast bilateral filtering is utilised to eliminate the noises in the input video frames. The proposed study introduces the E-SWBF scheme in the pre-processing stage to enhance image quality. Utilising the wiener filter helps to reduce the mean square error (MSE) and can manage both noise and degradation functions. The fast bilateral filtering can smooth the given input images by reducing the noise. The Wiener filter is one of the statistical filters that generates an appropriate signal estimation from a noisy time order. In the general Wiener filter, the kernel is tuned to integrate detail and edge preservation. An efficient noise reduction is attained by performing a local Gaussian Markov random field. In the additive noise, the reference target is integrated and is represented as,
Deconvolution of blast vibration signals by wiener filtering
Published in Inverse Problems in Science and Engineering, 2018
The Wiener filter is also known as the ‘least squares’ filter or the ‘optimum least squares’ filter. According to Equation (4), the desired output is a delta function. On the other hand, the calculated output is a function that was calculated using the known impulse train information and one assumed function called a Wiener filter . Once the desired and calculated outputs are compared using a least squares procedure, the optimum Wiener filter will result in the function that produces the minimum error. This can be viewed as a trial and error procedure which indicates that the location of the desired delta function is unknown so this variable is a key element to solve the problem.
Toeplitz matrices for LTI systems, an illustration of their application to Wiener filters and estimators
Published in International Journal of Systems Science, 2018
As an example to illustrate the Toeplitz method for LTI systems, consider the Wiener filter problem. We consider the traditional polynomial approach first and then the Toeplitz method. The Wiener filter is the optimal estimator for a random signal contaminated by random noise. The noise can be either additive white, coloured or both. We distinguish between different kinds of estimators which gives rise to filters, smoothers or predictors. The simplest formulation is the filter, but a smoother will give lower estimation error at the expense of a time-delay. Mostly, this time-delay is of little consequence in signal processing but can be a problem for stability in closed-loop control-systems. Predictors give the worse estimate of all but have less information to use than the other two methods. Predictors are used in many areas including optimal control (Rossiter, 2003).