Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of se...
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ISBN:
(纸本)9780819472946
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We s...
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ISBN:
(纸本)9780819468451
We formulate in a simple fashion the concept of invariance for a linear system. We show that one must define what we call an "associated Hermitian operator"' which commutes with the system function. We show that it is this Hermitian operator that defines the invariance and also determines the appropriate transform and other connections between input and output relations.
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extract...
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ISBN:
(纸本)9780819468451
A model for an infrared (M) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
Given the moments of a time-frequency distribution, one can, in principle, construct the characteristic function from which one then obtains the distribution by Fourier transformation. However, often one can not find ...
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ISBN:
(纸本)9780819468451
Given the moments of a time-frequency distribution, one can, in principle, construct the characteristic function from which one then obtains the distribution by Fourier transformation. However, often one can not find a closed form for the characteristic function and hence one can not obtain the distribution in a direct manner. We formulate the problem of constructing time-frequency representations from moments without first constructing the characteristic function. Our method is based on expanding the distribution in terms of a complete set of functions where the expansion coefficients are dependent directly on the moments. We apply the method to a case where the even moments are manifestly positive which is a necessary condition for obtaining a proper time-frequency representation.
In this paper, we consider the use of a seismic sensor array for the localization and tracking of a wideband moving source. The proposed solution consists of two steps: source Direction-Of-Arrival (DOA) estimation and...
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ISBN:
(纸本)9780819468451
In this paper, we consider the use of a seismic sensor array for the localization and tracking of a wideband moving source. The proposed solution consists of two steps: source Direction-Of-Arrival (DOA) estimation and localization via DOA estimates. Three DOA estimation methods are considered. The Covariance Matrix Analysis and the Surface Wave Analysis are previously published DOA estimation algorithms shown to be effective in the localization of a stationary wideband source. This paper investigates their performance on moving wideband sources. A novel DOA estimation algorithm, the Modified Kirlin's Method was also developed for the localization of a moving Source. The DOAs estimated by these algorithms are combined rising a least-squares optimization for source localization. The application of these algorithms to real-life data show the effectiveness of both the Surface Wave Analysis and the Modified Kirlin's Method in locating and tracking a wideband moving source.
We are presenting a new method for super resolution tracking of frequency modulated sinusoids in white noise. The method is specifically designed to handle the rapid transient problem, i.e. the problem of tracking a c...
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ISBN:
(纸本)9780819468451
We are presenting a new method for super resolution tracking of frequency modulated sinusoids in white noise. The method is specifically designed to handle the rapid transient problem, i.e. the problem of tracking a continuous, rapidly changing instantaneous frequency contour. The proposed method employs to components: 1) an adaptive generalized scale transform 1, 2 which applies a localized change of time-frequency coordinates within the given signal, and 2) an estimation of signal parameters by rotational invariance techniques 3 (ESPRIT). With experiments we have shown that the proposed method provides a significantly higher estimation accuracy than conventional methods. 3 With an optimal choice of transform parameters the estimation error can be reduced dramatically. Error reductions of over 40% have been observed.
We consider the problem of recovering signals from noisy indirect observations under the additional a priori information that the signal is believed to be slowly varying except at all unknown number of points where it...
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ISBN:
(纸本)9780819468451
We consider the problem of recovering signals from noisy indirect observations under the additional a priori information that the signal is believed to be slowly varying except at all unknown number of points where it may have discontinuities of unknown size. The model problem is a linear deconvolution problem. To take advantage of the qualitative prior information available, we use a non-stationary Markov model with the variance of the innovation process also unknown, and apply Bayesian techniques to estimate both the signal and the prior variance. We propose a fast iterative method for computing a MAP estimates and we show that, with a rather standard choices of the hyperpriors, the algorithm produces the fixed point iterative solutions of the total variation and of the Perona-Malik regularization methods. We also demonstrate that, unlike the non-statistical estimation methods, the Bayesian approach leads to a very natural reliability assessment of edge detection by a Markov Chain Monte Carlo (MCMC) based analysis of the posterior.
We discuss the application of time-frequency analysis to biomechanical-type signals, and in particular to signals that would be encountered in the study of rotation rates of bicycle pedaling. We simulate a number of s...
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ISBN:
(纸本)9780819468451
We discuss the application of time-frequency analysis to biomechanical-type signals, and in particular to signals that would be encountered in the study of rotation rates of bicycle pedaling. We simulate a number of such signals and study how well they are represented by various time-frequency methods. We show that time-frequency representations track very well the instantaneous frequency even when there are very fast changes. In addition. we do a correlation analysis between time-series whose instantaneous frequency is changing and show that the traditional correlation coefficient is insufficient to characterize the correlations. We instead show that the correlation coefficient should be evaluated directly from the instantaneous frequencies of the time series, which can be easily estimated from their time-frequency distributions.
Multi-modeling is a recent tool proposed for modeling complex nonlinear systems by the use of a combination of relatively simple set of local models. Due to their simplicity, linear local models are mainly used in suc...
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ISBN:
(纸本)9789728865849
Multi-modeling is a recent tool proposed for modeling complex nonlinear systems by the use of a combination of relatively simple set of local models. Due to their simplicity, linear local models are mainly used in such structures. In this work, multi-models having polynomial local models are described and applied in system identification. Estimation of model's parameters is carried out using least squares algorithms which reduce considerably computation time as compared to iterative algorithms. The proposed methodology is applied to recurrent models implementation. NARMAX and NOE multi-models are implemented and compared to their corresponding neural network implementations. Obtained results show that the proposed recurrent multi-model architectures have many advantages over neural network models.
We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well sui...
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ISBN:
(纸本)0819463922
We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well suited to take into account nonstationary situations. The advantage of our measure is that it is a local property unlike the standard SNR which is a single number representing the total duration of the signal. We simulated a number of cases to show that our measure is more indicative of the noise and signal level for nonstationary situations.
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