Wiener filter

This paper attempts to undertake the study of Restored Gaussian Blurred Images. by using four types of techniques of deblurring image as Wiener filter, Regularized filter, Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image with Different values of Size and Alfa and then corrupted by Gaussian noise. The same is applied to the remote sensing image and they are compared with one another, So as to choose the base technique for restored or deblurring image.This paper also attempts to undertake the study of restored Gaussian blurred image with no any information about the Point Spread Function (PSF) by using same four techniques after execute the guess of the PSF, the number of iterations and the weight threshold of it. To choose the base guesses for restored or deblurring image of this techniques.

Ce TP a pour objectif de prendre en main le logiciel MATLAB. Les notions traitées sont la manipulation de variables aléatoires et de vecteurs, l’utilisation de fonctions, la représentation graphique, la génération de nombres aléatoires, le calcul d’écart-type de la variance, le calcul de la variance et la corrélation et la génération des signaux aléatoires en utilisant des systèmes linéaires invariants dans le temps. En ce qui concerne la 2 ème  partie, on étudie les propriétés du filtrage adaptatif (filtre de Wiener) en utilisant l’algorithme LMS.

In diagnosis of medical images, operations such as feature extraction and object recognition plays the key role.. These operations will become difficult if the images are corrupted with noises. Several types of noise were introduced in the images during image acquisition, transfer & storage. The main objective is to remove the noise from the input image. Image Denoising is an utmost challenge for Researchers, developing Image denoising algorithms is a difficult task, since fine details in a medical image should not be destroyed during noise removal during the diagnosis of information. Medical image focuses on the speckle noise. So, several denoising filters are implemented and performance are compared to find the optimum filter. To the input image noise is added and various filtering are applied to remove the noise. The filters used are Wiener filter, Adaptive Fuzzy filter and Vector Median Filter. The frequency domain is used to improve the quality of the denoising method. As the first step Contourlet transform is applied to the noisy image. Further, several filters are applied on the transformed image for the effective removal of noise. The performance of the filters are compared based on PSNR, SSIM, IQI.

In the field of artificial intelligence, Adaptive Learning Technique refers to the combination of artificial neural networks. In this research paper the Adaptive Learning Technique has been implemented to carry out the Detection and Localization of Sound (S). In this technique two methods are used to detect the pure sound, In the first method wiener filter are used to reduce the amount of noise in a signal and minimize the mean square error (M.S.E), And in the second method wiener with bacterial foraging optimization are used for effectiveness in sound. These proposed methods are compared and the results reveal its superiority.

It is well known that encryption provides secure channels for communicating entities. However, due to lack of covertness on these channels, an eavesdropper can identify encrypted streams through statistical tests and capture them for further cryptanalysis. Hence, the communicating entities can use steganography to achieve covertness. In this paper we propose a new form of multimedia steganography called data masking.

This chapter reviews the solutions for the discrete-time, linear stationary filtering problems that are attributed to Wiener [1] and Kolmogorov [2]. As in the continuous-time case, a model-based approach is employed. Here, a linear model is specified by the coefficients of the input and output difference equations. It is shown that the same coefficients appear in the system’s (frequency domain) transfer function. In other words, frequency domain model representations can be written down without background knowledge of z-transforms.

In the 1960s and 1970s, continuous-time filters were implemented on analogue computers. This technology has been largely discontinued for two main reasons. First, analogue multipliers and op amp circuits exhibit poor performance whenever (temperature-sensitive) calibrations become out of date. Second, updated software releases are faster to turn around than hardware design iterations. Continuous-time filters are now routinely implemented using digital computers, provided that the signal sampling rates and data processing rates are sufficiently high. Alternatively, continuous-time model parameters may be converted into discrete-time and differential equations can be transformed into difference equations. The ensuing discrete-time filter solutions are then amenable to more economical implementation, namely, employing relatively lower processing rates.

The discrete-time Wiener filtering problem is solved in the frequency domain. Once again, it is shown that the optimum minimum-mean-square-error solution is found by completing the square. The optimum solution is noncausal, which can only be implemented by forward and backward processes. This solution is actually a smoother and the optimum filter is found by taking the causal part.

The developments rely on solving a spectral factorisation problem, which requires pole-zero cancellations. Therefore, some pertinent discrete-time concepts are introduced in Section 2.2 prior to deriving the filtering results. The discussion of the prerequisite concepts is comparatively brief since it mirrors the continuous-time material introduced previously. In Section 2.3 it is shown that the structure of the filter solutions is unchanged – only the spectral factors are calculated differently.

This research work proposes and explore
different wavelets methods in digital image denoising.
Using several wavelets threshold technique such as
SUREShrink, VisuShrink, and BayesShrink in search for
efficient image denoising method. In this paper, we extend
the existing technique and provide a comprehensive
evaluation of the proposed method. Wiener filtering
technique is the proposed method which was compared
and analysed, while the performance of all the techniques
were compared to ascertain the most efficient method.

In a fast growing industrial world, carriers are required to carry products from one manufacturing plant to another which are usually in different buildings or separate blocks. This study intends to automate this sector using vision controlled mobile robots instead of laying railway tracks which are both expensive and inconvenient. To achieve this purpose an autonomous robot with computer vision as its primary sensor for gaining information about its environment for path following is developed. The proposed Line Follower Robot (LFR) consists of web cam mounted on the vehicle and connected to Matlab platform. A PID control algorithm will be applied to adjust the robot on the line. The proposed LFR is accomplished through the following stages: Firstly, the image is acquired using the web cam. The acquired RGB image is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using histogram equalization ...

Optimal filtering is concerned with designing the best linear system for recovering data from noisy measurements. It is a model-based approach requiring knowledge of the signal generating system. The signal models, together with the noise statistics are factored into the design in such a way to satisfy an optimality criterion, namely, minimising the square of the error.

A prerequisite technique, the method of least-squares, has its origin in curve fitting. Amid some controversy, Kepler claimed in 1609 that the planets move around the Sun in elliptical orbits [1]. Carl Freidrich Gauss arrived at a better performing method for fitting curves to astronomical observations and predicting planetary trajectories in 1799 [1]. He formally published a least-squares approximation method in 1809 [2], which was developed independently by Adrien-Marie Legendre in 1806 [1]. This technique was famously used by Giusseppe Piazzi to discover and track the asteroid Ceres using a least-squares analysis which was easier than solving Kepler’s complicated nonlinear equations of planetary motion [1]. Andrey N. Kolmogorov refined Gauss’s theory of least-squares and applied it for the prediction of discrete-time stationary stochastic processes in 1939 [3]. Norbert Wiener, a faculty member at MIT, independently solved analogous continuous-time estimation problems. He worked on defence applications during the Second World War and produced a report entitled Extrapolation, Interpolation and Smoothing of Stationary Time Series in 1943. The report was later published as a book in 1949 [4].

Wiener derived two important results, namely, the optimum (non-causal) minimum-mean-square-error solution and the optimum causal minimum-mean-square-error solution [4] – [6]. The optimum causal solution has since become known at the Wiener filter and in the time-invariant case is equivalent to the Kalman filter that was developed subsequently. Wiener pursued practical outcomes and attributed the term “unrealisable filter” to the optimal non-causal solution because “it is not in fact realisable with a finite network of resistances, capacities, and inductances” [4]. Wiener’s unrealisable filter is actually the optimum linear smoother.

The optimal Wiener filter is calculated in the frequency domain. Consequently, Section 1.2 touches on some frequency-domain concepts. In particular, the notions of spaces, state-space systems, transfer functions, canonical realisations, stability, causal systems, power spectral density and spectral factorisation are introduced. The Wiener filter is then derived by minimising the square of the error. Three cases are discussed in Section 1.3. First, the solution to general estimation problem is stated. Second, the general estimation results are specialised to output estimation. The optimal input estimation or equalisation solution is then described. An example, demonstrating the recovery of a desired signal from noisy measurements, completes the chapter.