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About 3,734 papers for “Fast wavelet transform”

Fast Wavelet Transform

Indu Solomon, Uttam Kumar · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · 2023

Cited by 0

Fast Modulated Wavelet Transform

Jiwani, Lalit, Joshi, Shiv, Visweswaran, G S · Zenodo (CERN European Organization for Nuclear Research) · 2010

Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark, 2010

Cited by 0Open access

The Fast Wavelet Transform (FWT)

Keith G. Boyer · 1995

A/mathematical/basis/for/the/construction/of/the/fast wavelet/transform/(FWT),/based/on/the/wavelets/of Daubechies,/is/given.//A/contrast/is/made/between/the continuous/wavelet/transform/and/the/discrete/wavelet transform/that/provides/the/fundamental/structure/for/the fast/wavelet/transform/algorithm.//The/wavelets considered/here/lead/to/orthonormal/bases.//To/realize the/orthonormality/of/these/bases,/the/Fourier/transform is/used/to/construct/equivalent/realizations/of/the wavelet/recursions/in/the/spectral/domain.//These spectral/representations/lead/to/a/complementary/pair/of filter/transfer/functions,/from/which/the/inverse/Fourier/ transform/can/be/used/to/generate/equivalent/Daubechies' convolution/coefficients.//The/discrete/wavelet transform,/generated/from/the/convolution/filter operations,/is/incorporated/into/a/recursive/filter decimation/algorithm/that/is/the/FWT.//Finally,/the/FWT is/applied/to/the/image/compression/problem.//// This/abstract/accurately/represents/the/c...

Cited by 0

Implementing Fast-Haar Wavelet transform on

Javier Medina, Carlos Pinilla, Luis Joyanes · 2016

original Ikonos images to perform image fusion: qualitative assessment Implementación de la Transformada Rápida de Haar con una imagen original Ikonos para la fusión de imágenes: evaluación cualitativa

Cited by 0

An Architecture For Fast Wavelet Transform

Charles Chang And, Charles C. Chang, Jyh--charn Liu · 2007

In this paper we propose a simple yet effective architecture for fast wavelet transform. Our system incorporates the use of fixed point numbers which results in a simpler design than the conventional processor with a floating point arithmetic unit. Calculations are further reduced using our thresholding-in-calculations (TIC) technique to omit calculations of small terms that are negligible to the accumulated sum. This reduction exhibits a significant performance gain while maintaining reasonable accuracy.

Cited by 0

Fast Correlations in Wavelet Transform Domain

Anna Z. Baraniecki · 2014

Abstract:- In response to rapidly growing use of discrete wavelet transform in a variety of digital signal processing applications, this paper describes algorithms to facilitate the implementation of correlations on wavelet representations. Correlation is a fundamental operation for comparing two signals or two images. The technique is especially applicable for comparing images or image fragments with respect to all translations of a full image. The algorithm described in this paper builds on image correlation theorem, which shows that subsamples of correlations of two signals can be obtained from a sum of correlations of subbands of wavelet representations of those signals. Images are mapped into wavelet domain. Cascaded filter banks in the realization of wavelet transform can be implemented in transform domain using any transform that has cyclic convolution property. Improved computational efficiency is a result of replacing subsequent transformations, decimations or interpolations and inverse transformations by simple additions or replications and utilizing products of intermidiate results to obtain correlation in subbands. Key-Words: wavelet transform, multiresolution analysis, fast algorithms, correlation. CSCC'99 Proc.pp.2011-2015 1.

Cited by 0

A fast discrete periodic wavelet transform

Neil Getz, Neil H. Getz · 1992

The discrete wavelet transform (DWT) is extended to functions on the discrete circle to create a fast and complete discrete periodic wavelet transform (DPWT) for bounded periodic sequences. This extension also solves the problem of non-invertibility that arises in the application of the DWT to finite-dimensional sequences and provides the proper theoretical setting for previous incomplete solutions to the invertibility problem. It is proven that the same filter coefficients used with the DWT to create orthonormal wavelets on compact support in L ∞ may be incorporated through the DPWT to create an orthonormal basis of discrete periodic wavelets. By exploiting transform symmetry and periodicity, easily implementable, fast, and recursive synthesis and analysis algorithms are derived. Matlab

Cited by 0

Fast hologram calculation using wavelet transform

Tomoyoshi Shimobaba, Shota Yamada, Takashi Kakue et al. · Tenth International Conference on Information Optics and Photonics · 2018

To further accelerate the calculations associated with point-cloud-based holograms, wavelet shrinkage-based superpositIon (WASABI) has been proposed. Wavelet shrinkage eliminates the small wavelet coefficient values of the light distribution emitted from a point cloud, resulting in an approximated light distribution calculated from a few representative wavelet coefficients. Although WASABI can accelerate the hologram calculations, the approximated light distribution tends to lose the high-frequency components. To address this issue, random sampling was applied to the light distribution.

Cited by 3

Fast Algorithms for Wavelet Transform Computation

Metin Akay · HAL (Le Centre pour la Communication Scientifique Directe) · 2010

<P>This chapter contains sections titled: <UL> <LI> <P>Introduction</P> </LI> <LI> <P>Multiresolution and Two-Scale Equations</P> </LI> <LI> <P>The Initial Signal Approximation</P> </LI> <LI> <P>The Discrete Wavelet Transform (DWT)</P> </LI> <LI> <P>The DWT For WS Computation</P> </LI> <LI> <P>The DWT For CWT Computation</P> </LI> <LI> <P>Efficient Implementations of the DWT</P> </LI> <LI> <P>Faster DWT Algorithms</P> </LI> <LI> <P>Other Algorithms for CWT Computation</P> </LI> <LI> <P>Conclusions</P> </LI> </UL> </P> <P>This chapter contains sections titled: <UL> <LI> <P>References</P> </LI> </UL> </P>

Cited by 4Open access

Image Compression Using Fast Wavelet Transform

2016

In general, image compression reduces the number bits required to represent an image. The main significance of image compression is that the quality of the image is preserved. This in turn increases the storage space and thereby the volume of the data that can be stored. Image compression is the application of data compression technique on digital images. Wavelet Transform based image compression remain the most common among diverse techniques proposed earlier. Data compression which can be lossy or lossless is required to decrease the storage requirement and better data transfer rate. One of the best image compression techniques is using wavelet transform. It is comparatively new and has many advantages over others. Wavelet transform uses a large variety of wavelets for decomposition of images. The state of the art coding techniques like EZW, SPIHT (set partitioning in hierarchical trees) and EBCOT(embedded block coding with optimized truncation)use the wavelet transform as basic and common step for their own further technical advantages. The wavelet transform results therefore have the importance which is dependent on the type of wavelet used.In our thesis we have used different wavelets to perform the transform of a test image and the results have been discussed and analyzed. For the implementation of this proposed work we use Image Processing Toolbox under the Matlab software.

Cited by 0

Fast directional continuous spherical wavelet transform algorithm

J. D. Mcewen, M. P. Hobson, D. J. Mortlock et al. · 2007

Abstract — The construction of a spherical wavelet analysis through the inverse stereographic projection of the Euclidean planar wavelet framework, originally introduced by Wiaux et al. [1], is described and extended to anisotropic dilations defined on the sphere in two orthogonal directions. Fast algorithms for performing the directional continuous wavelet analysis on the sphere are presented. The anisotropic fast algorithm provides a saving of O ( √ Npix) over a direct quadrature implementation, for Npix pixels on the sphere, and allows one to perform an anisotropic spherical wavelet analysis of a 10 6 pixel map on a personal computer. Index Terms — Wavelet transforms, spheres, convolution. I.

Cited by 0

A fast directional continuous spherical wavelet transform

Jason D. McEwen, M. P. Hobson, A. Lasenby et al. · arXiv (Cornell University) · 2004

A fast algorithm for Antoine and Vandergheynst's (1998) directional continuous spherical wavelet transform (CSWT) is presented. Computational requirements are reduced by a factor of O(\sqrt{N}), when N is the number of pixels on the sphere. The spherical Mexican hat wavelet Gaussianity analysis of the WMAP 1-year data performed by Vielva et al. (2003) is reproduced and confirmed using the fast CSWT. The proposed extension to directional analysis is inherently afforded by the fast CSWT algorithm.

Cited by 2Open access

Fast surface interpolation using multiresolution wavelet transform

Ming-Haw Yaou, Wen-Thong Chang · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1994

Discrete formulation of the surface interpolation problem usually leads to a large sparse linear equation system. Due to the poor convergence condition of the equation system, the convergence rate of solving this problem with iterative method is very slow. To improve this condition, a multiresolution basis transfer scheme based on the wavelet transform is proposed. By applying the wavelet transform, the original interpolation basis is transformed into two sets of bases with larger supports while the admissible solution space remains unchanged. With this basis transfer, a new set of nodal variables results and an equivalent equation system with better convergence condition can be solved. The basis transfer can be easily implemented by using an QMF matrix pair associated with the chosen interpolation basis. The consequence of the basis transfer scheme can be regarded as a preconditioner to the subsequent iterative computation method. The effect of the transfer is that the interpolated surface is decomposed into its low-frequency and high-frequency portions in the frequency domain. It has been indicated that the convergence rate of the interpolated surface is dominated by the low-frequency portion. With this frequency domain decomposition, the low-frequency portion of the interpolated surface can be emphasized. As compared with other acceleration methods, this basis transfer scheme provides a more systematical approach for fast surface interpolation. The easy implementation and high flexibility of the proposed algorithm also make it applicable to various regularization problems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 59

A fast wavelet transform-domain LMS algorithm

S. Attallah, Mohamed Najim · 2002

This paper presents a fast wavelet transform-domain LMS (FWTDLMS) algorithm whose computational complexity is very low as compared to what has been reported so far, because it is completely independent from the adaptive filter order. In our method, the reduction in the computational load has been achieved by exploiting the redundancy in the computation of the wavelet coefficients as the data window slides on by one sample at each new iteration. As a result, the complexity of the wavelet transform is reduced from O(mN) where m is the wavelet filter length and N is the adaptive filter order to O(m(m+M)/2) where M (M<m) is the number of subbands in the maximally decimated filter bank. In addition, we have introduced a recursion in the estimation of the energy of the wavelet coefficients. This complexity then becomes O(2N+m(m+M)/2+m+4M), which is comparable to time-domain LMS complexity when short wavelet filters (m/spl Lt/N) are used.

Cited by 12

Fast algorithms for the continuous wavelet transform

Olivier Rioul · 1991

It is shown that filter banks arise naturally when implementing the continuous wavelet transform (CWT). The conditions under which the CWT can be computed exactly using discrete filter banks are determined, and fast CWT algorithms are derived. The complexity of the resulting algorithms increases linearly with the number of octaves. They are easily implemented by repetitive application of identical cells, to which various methods are applied for reducing the number of operations: FFT (fast Fourier transform) algorithms are most efficient for large filter lengths; for small lengths, fast running FIR (finite impulse response) algorithms are preferred.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 10