Fast computation of convolution operations via low-rank approximation

Authors:
Tristan A. Hearn;Lothar Reichel
Affiliations:
-;-
Venue:
Applied Numerical Mathematics
Year:
2014

Citing 10
Cited 0

Topics in matrix analysis

Topics in matrix analysis
Some algorithms for approximating convolutions

Computer Vision, Graphics, and Image Processing
Accelerating 3D convolution using graphics hardware (case study)

VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Face Recognition: Features Versus Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of the spectral and the discrete singular convolution schemes for the KdV-type equations

Journal of Computational and Applied Mathematics
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
FFT and Convolution Performance in Image Filtering on GPU

IV '06 Proceedings of the conference on Information Visualization
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)

Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
An autocorrelation pitch detector and voicing decision withconfidence measures developed for noise-corrupted speech

IEEE Transactions on Signal Processing
Blind MIMO channel identification from second order statistics using rank deficient channel convolution matrix

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Methods for the approximation of 2D discrete convolution operations are derived for the case when a low-rank approximation of one of the input matrices is available. Algorithms based on explicit computation of discrete convolution and on the Fast Fourier Transform are both described. Applications of the described methods to the computation of cross-correlation and autocorrelation are discussed and illustrated by examples. Both theory and numerical experiments show that the use of low-rank approximations makes it possible to determine accurate approximations of convolution, cross-correlation, and autocorrelation operations at competitive speeds.