The proceedings contains 14 papers. Topics discussed include nonlinearprocessing techniques, video and film restoration, system identification, filtering, film sequences, image distortions, colour images and signal m...
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The proceedings contains 14 papers. Topics discussed include nonlinearprocessing techniques, video and film restoration, system identification, filtering, film sequences, image distortions, colour images and signal modeling.
In 1998, with the advent of Telecines outputting High Definition video, the authors were asked to develop film-type noise reduction algorithms that could be implemented in real-time hardware. The aim was to be able to...
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In 1998, with the advent of Telecines outputting High Definition video, the authors were asked to develop film-type noise reduction algorithms that could be implemented in real-time hardware. The aim was to be able to automatically detect and remove blotches and scratches, and reduce the levels of film grain noise if required. By using a combination of spatial and motion-compensated temporal techniques, algorithms have been successfully developed to remove all these types of noise with little human intervention. Motion compensation, though expensive in terms of hardware, especially at high resolutions, is a well practised technique and has proved invaluable for detecting and removing blotches, and for grain reduction. This paper will concentrate on the description of the grain reduction algorithm. Grain reduction is the last step in the overall film noise reducer, and the prior removal of scratches and blotches enables more accurate grain reduction and detail preservation. Because of the random nature of grain noise, it is possible that grain cannot reliably be distinguished temporally, from detail, but is clearly visible spatially. Therefore, spatial processing is required as well as motion-compensated temporal processing. Using a mixture of temporal and spatial non-linear filtering techniques, and temporal spectral analysis comparisons, a balance of detail preservation and grain noise reduction can be gained, with minimal user intervention.
This paper describes two different applications of non-linearsignalprocessing. In both cases, computational simplicity was a major goal and a non-linear approach significantly increased performance. The two applicat...
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This paper describes two different applications of non-linearsignalprocessing. In both cases, computational simplicity was a major goal and a non-linear approach significantly increased performance. The two applications (PSK demodulation of a noisy signal from sign-only data, and frequency estimation of a single complex tone) are both of practical importance. It is also hoped that these two examples will encourage designers of real systems to investigate non-linear approaches. Finally, the examples may be of interest to theoreticians since in neither case has a full theoretical analysis been completed.
A general predictive frequency condition monitoring method which is capable of explicitly identifying the presence, nature and magnitude of a fault condition is described. The optimal detection mechanism of this metho...
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A general predictive frequency condition monitoring method which is capable of explicitly identifying the presence, nature and magnitude of a fault condition is described. The optimal detection mechanism of this method is the nonlinear Higher Order Statistical techniques. The method is illustrated by practical investigation of the condition monitoring of an induction machine.
This article reviews the computation of image distortions produced by mean, median, and mode filter using continuum models. It then shows how, in the case of median filters, it can be extended using discrete models to...
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This article reviews the computation of image distortions produced by mean, median, and mode filter using continuum models. It then shows how, in the case of median filters, it can be extended using discrete models to give more predictions that are in line with experimental measurements.
Most adaptive image and signalprocessing tasks are performed on specialist digital signalprocessing chips. These devices are highly optimised for efficient computation of the core multiply and accumulate operations ...
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Most adaptive image and signalprocessing tasks are performed on specialist digital signalprocessing chips. These devices are highly optimised for efficient computation of the core multiply and accumulate operations required by current algorithms. Attempts to synthesise these types of algorithms on FPGAs have resulted in few competitive implementations. FPGAs generally fail to realise efficient arithmetic functions except in the most constrained cases such as constant coefficient multipliers. The approach adopted in this paper is based on the use of stack filters that avoid these difficulties by employing logical algorithms that do not rely on any arithmetic functions.
It is shown that the minimum-mean square estimate of the parameters of a Volterra model can be obtained in closed form when the input is circularly symmetric. The proposed method is applicable to systems with arbitrar...
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It is shown that the minimum-mean square estimate of the parameters of a Volterra model can be obtained in closed form when the input is circularly symmetric. The proposed method is applicable to systems with arbitrary orders and memory. Theoretical results are illustrated via Monte-Carlo simulations.
A research program is conducted to unify existing methods of signalprocessing and to identify areas of research where new techniques are required. The focus of study includes nonlinear and nonstationary time series e...
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A research program is conducted to unify existing methods of signalprocessing and to identify areas of research where new techniques are required. The focus of study includes nonlinear and nonstationary time series estimation, forecasting and changepoint modeling, nonlinearsignalprocessing in econometrics and financial time series, dynamical systems, environmental applications and spatial data analysis, and applications of Bayesian signalprocessing. A discussion and experimentation of existing and new-developed technology are also included.
In this paper, an introduction to Bayesian methods in signalprocessing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions ...
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In this paper, an introduction to Bayesian methods in signalprocessing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions for the model probabilities of two simple models. The idea of marginal estimation of certain model parameter is then introduced and expressions are derived for the marginal probability densities for frequencies in white Gaussian noise and a Bayesian approach to general changepoint analysis is given. Numerical integration methods are introduced based on Markov chain Monte Carlo techniques and the Gibbs sampler in particular.
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