Abstract
In image processing, convolution is a frequently used operation. It is an important tool for performing basic image enhancement as well as sophisticated analysis. Naturally, due to its necessity and still continually increasing size of processed image data there is a great demand for its efficient implementation. The fact is that the slowest algorithms (that cannot be practically used) implementing the convolution are capable of handling the data of arbitrary dimension and size. On the other hand, the fastest algorithms have huge memory requirements and hence impose image size limits. Regarding the convolution of huge images, which might be the subtask of some more sophisticated algorithm, fast and correct solution is essential. In this paper, we propose a fast algorithm implementing exact computation of the shift invariant convolution over huge multi-dimensional image data.
Chapter PDF
Similar content being viewed by others
References
Parker, J.R.: Algorithms for image processing and computer vision. Wiley, Chichester (1996)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)
Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. Int. J. Comput. Vision 5(1), 77–104 (1990)
Verveer, P.J.: Computational and optical methods for improving resolution and signal quality in fluorescence microscopy, PhD Thesis. Delft Technical Univ. (1998)
Jensen, J.A., Munk, P.: Computer phantoms for simulating ultrasound B-mode and cfm images. Acoustical Imaging 23, 75–80 (1997)
Pratt, W.K.: Digital Image Processing. Wiley, Chichester (1991)
Jähne, B.: Digital Image Processing, 6th edn. Springer, Heidelberg (1997)
Smith, S.W.: Digital Signal Processing. Newnes (2002)
Jan, J.: Digital Signal Filtering, Analysis and Restoration. Telecommunications Series. INSPEC, Inc. (2000) ISBN: 0852967608
Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proceedings of the IEEE 93(2), 216–231 (2005); Special issue on Program Generation, Optimization, and Platform Adaptation
Heideman, M., Johnson, D., Burrus, C.: Gauss and the history of the fast fourier transform. IEEE ASSP Magazine 1(4), 14–21 (1984) ISSN: 0740-7467
Svoboda, D., Kozubek, M., Stejskal, S.: Generation of digital phantoms of cell nuclei and simulation of image formation in 3d image cytometry. Cytometry Part A 75A(6), 494–509 (2009)
Rexilius, J., Hahn, H.K., Bourquain, H., Peitgen, H.-O.: Ground Truth in MS Lesion Volumetry – A Phantom Study. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 546–553. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Svoboda, D. (2011). Efficient Computation of Convolution of Huge Images. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24085-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-24085-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24084-3
Online ISBN: 978-3-642-24085-0
eBook Packages: Computer ScienceComputer Science (R0)