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@inbook{992722,
author = {Karas, Pavel and Svoboda, David},
address = {Rijeka (CRO)},
booktitle = {Design and Architectures for Digital Signal Processing},
doi = {http://dx.doi.org/10.5772/3456},
edition = {1st ed.},
keywords = {convolution; algorithms; FFT; separable convolution; recursive filters; parallelization; decomposition},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Rijeka (CRO)},
isbn = {978-953-51-0874-0},
pages = {179-208},
publisher = {InTech},
title = {Algorithms for Efficient Computation of Convolution},
url = {http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution},
year = {2013}
}
TY - CHAP
ID - 992722
AU - Karas, Pavel - Svoboda, David
PY - 2013
TI - Algorithms for Efficient Computation of Convolution
VL - Neuveden
PB - InTech
CY - Rijeka (CRO)
SN - 9789535108740
KW - convolution
KW - algorithms
KW - FFT
KW - separable convolution
KW - recursive filters
KW - parallelization
KW - decomposition
UR - http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution
L2 - http://www.intechopen.com/books/design-and-architectures-for-digital-signal-processing/algorithms-for-efficient-computation-of-convolution
N2 - Convolution is an important mathematical tool in both fields of signal and image processing. It is em-ployed in filtering, denoising, edge detection, correlation, compression, deconvolution, simulation, and in many other applications. Although the concept of convolution is not new, the efficient computation of convolution is still an open topic. As the amount of processed data is constantly increasing, there is considerable request for fast manipulation with huge data. Moreover, there is demand for fast algorithms which can exploit computational power of modern parallel architectures. The aim of this chapter is to review the algorithms and approaches for computation of convolution with regards to various properties such as signal and kernel size or kernel separability (when pro-cessing n-dimensional signals). Target architectures include superscalar and parallel processing units (namely CPU, DSP, and GPU), programmable architectures (e.g. FPGA), and distributed systems (such as grids). The structure of the chapter is designed to cover various applications with respect to the signal size, from small to large scales.
ER -
KARAS, Pavel and David SVOBODA. Algorithms for Efficient Computation of Convolution. In \textit{Design and Architectures for Digital Signal Processing}. 1st ed. Rijeka (CRO): InTech, 2013, p.~179-208. ISBN~978-953-51-0874-0. Available from: https://dx.doi.org/10.5772/3456.
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