A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
On Two Variants of an Algebraic Wavelet Preconditioner
SIAM Journal on Scientific Computing
Parallel Wavelet Transform for Large Scale Image Processing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A parallel implementation of the 2-D discrete wavelet transformwithout interprocessor communications
IEEE Transactions on Signal Processing
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In this paper we propose a strategy of partial data replication for efficient parallel computing of the Discrete Wavelet Transform in a distributed memory environment. The key is to avoid the communications needed between computation of different wavelet levels, by replicating part of the data and part of the computations, avoiding completely communications (except maybe at the setup phase). A similar idea was proposed in a paper by Chaver et al.; however, they proposed to replicate completely the data, which can require too much memory in each processor. In this work we have determined exactly how many data items shall be needed for each processor, in order to compute the DWT without extra communications and using only the memory strictly necessary.