Journal ArticleDOI
Filtering noise from images with wavelet transforms.
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TLDR
A new method of filtering MR images is presented that uses wavelet transforms instead of Fourier transforms, which does not reduce the sharpness of edges but does eliminate any small structures that are similar in size to the noise eliminated.Abstract:
A new method of filtering MR images is presented that uses wavelet transforms instead of Fourier transforms. The new filtering method does not reduce the sharpness of edges. However, the new method does eliminate any small structures that are similar in size to the noise eliminated. There are many possible extensions of the filter.read more
Citations
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Journal ArticleDOI
Splines: a perfect fit for signal and image processing
TL;DR: The article provides arguments in favor of an alternative approach that uses splines, which is equally justifiable on a theoretical basis, and which offers many practical advantages, and brings out the connection with the multiresolution theory of the wavelet transform.
Journal ArticleDOI
Wavelet transform domain filters: a spatially selective noise filtration technique
TL;DR: A spatially selective noise filtration technique based on the direct spatial correlation of the wavelet transform at several adjacent scales is introduced and can reduce noise contents in signals and images by more than 80% while maintaining at least 80% of the value of the gradient at most edges.
Journal ArticleDOI
MRI segmentation: Methods and applications
Laurence P. Clarke,R.P. Velthuizen,M.A. Camacho,John J. Heine,M. Vaidyanathan,Lawrence O. Hall,R.W. Thatcher,Martin L. Silbiger +7 more
TL;DR: The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter- and intra-observer variations inherent to supervised methods.
Journal ArticleDOI
A review of wavelets in biomedical applications
Michael Unser,Akram Aldroubi +1 more
TL;DR: The wavelet properties that are the most important for biomedical applications are described and an interpretation of the the continuous wavelet transform (CWT) as a prewhitening multiscale matched filter is provided.
Journal ArticleDOI
Wavelet-based Rician noise removal for magnetic resonance imaging
TL;DR: A novel wavelet-domain filter that adapts to variations in both the signal and the noise is presented, which is especially problematic in low signal-to-noise ratio (SNR) regimes.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI
The Laplacian Pyramid as a Compact Image Code
Peter J. Burt,Edward H. Adelson +1 more
TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
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The wavelet transform, time-frequency localization and signal analysis
TL;DR: Two different procedures for effecting a frequency analysis of a time-dependent signal locally in time are studied and the notion of time-frequency localization is made precise, within this framework, by two localization theorems.
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Multiresolution approximations and wavelet orthonormal bases of L^2(R)
TL;DR: In this paper, the authors study the properties of multiresolution approximation and prove that it is characterized by a 2π periodic function, which is further described in terms of wavelet orthonormal bases.