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Adapting to Unknown Smoothness via Wavelet Shrinkage
, 1995We attempt to recover a function of unknown smoothness from noisy sampled data. We introduce a procedure, SureShrink, that suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: A threshold level is assigned to
D. Donoho, I. Johnstone
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IFAC Proceedings Volumes, 1994
Abstract This is a tutorial about nonparametric nonlinear system identification. Advantages and limitations of this approach are discussed from the engineer’s point of view. Classical as well as modem techniques are discussed. Both practical and mathematical issues are investigated.
P-Y. Glorennec +4 more
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Abstract This is a tutorial about nonparametric nonlinear system identification. Advantages and limitations of this approach are discussed from the engineer’s point of view. Classical as well as modem techniques are discussed. Both practical and mathematical issues are investigated.
P-Y. Glorennec +4 more
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Wavelets, Wavelet Filters, and Wavelet Transforms [PDF]
, 2013 Spectral characteristics of speech are known to be particularly useful in describing a speech signal such that it can be efficiently reconstructed after coding or identified for recognition. The wavelets are considered one of such efficient methods for representing the spectrum of speech signals.
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On Sampling Theorem, Wavelets and Wavelet Transforms
Proceedings. IEEE International Symposium on Information Theory, 1993The classical Shannon sampling theorem has resulted in many applications and generalizations. From a multiresolution point of view, it provides the sine scaling function. In this case, for a band-limited signal, its wavelet series transform (WST) coefficients below a certain resolution level can be exactly obtained from the samples with a sampling rate
Xiang-Gen Xia, Zhen Zhang
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Wavelets and Wavelet Transform
2017Wavelet transforms are the most powerful and the most widely used tool in the field of image processing. Wavelet transform has received considerable attention in the field of image processing due to its flexibility in representing non-stationary image signals and its ability in adapting to human visual characteristics. Wavelet transform is an efficient
Soohwan Yu, Aparna Vyas, Joonki Paik
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Super-Wavelets and Decomposable Wavelet Frames
Journal of Fourier Analysis and Applications, 2005A wavelet frame is called decomposable whenever it is equivalent to a superwavelet frame of length greater than one. Decomposable wavelet frames are closely related to some problems on super-wavelets. In this article we first obtain some necessary or sufficient conditions for decomposable Parseval wavelet frames.
Gu, Qing, Han, Deguang, Heil, Chris
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Multisensor Image Fusion Using the Wavelet Transform
CVGIP: Graphical Models and Image Processing, 1995The goal of image fusion is to integrate complementary information from multisensor data such that the new images are more suitable for the purpose of human visual perception and computer-processing tasks such as segmentation, feature extraction, and ...
Hui Li, B. S. Manjunath, S. Mitra
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Wavelets and wavelets-design issues
Proceedings of ICCS '94, 2002This paper attempts to synthesise the wavelet theories to simple design procedures so that applied researchers can readily select or design wavelets with chosen characteristics for particular applications. The paper highlights the importance of the four most desirable characteristics of wavelets for use in digital signal processing, namely ...
Thong Nguyen, Dadang Gunawan
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Multifrequency channel decompositions of images and wavelet models
IEEE Transactions on Acoustics Speech and Signal Processing, 1989The author reviews recent multichannel models developed in psychophysiology, computer vision, and image processing. In psychophysiology, multichannel models have been particularly successful in explaining some low-level processing in the visual cortex ...
S. Mallat
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Wavelet-based statistical signal processing using hidden Markov models
IEEE Transactions on Signal Processing, 1998Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals.
M. Crouse, R. Nowak, Richard Baraniuk
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