Anisotropic Gaussian Filtering using Fixed Point Arithmetic

Christoph Lampert, Oliver Wirjadi
Proceedings of the 2006 International Conference on Image Processing (ICIP 2006), Pages 1565-1568, ICIP, 2006
(submitted version)


Gaussian filtering in one, two or three dimensions is among the most commonly needed tasks in signal and image pro- cessing. Finite impulse response filters in the time domain with Gaussian masks are easy to implement in either float- ing or fixed point arithmetic, because Gaussian kernels are strictly positive and bounded. But these implementations are slow for large images or kernels. With the recursive IIR- filters and FFT-based methods, there are at least two alter- native methods to perform Gaussian filtering in a faster way, but so far they are only applicable when floating-point hard- ware is available. In this paper, a fixed-point implementa- tion of recursive Gaussian filtering is discussed and applied to isotropic and anisotropic image filtering by making use of a non-orthogonal separation scheme of the Gaussian filter.




@inproceedings{ LAMP2006,
	Title = {Anisotropic Gaussian Filtering using Fixed Point Arithmetic},
	Author = {Christoph Lampert and Oliver Wirjadi},
	BookTitle = {Proceedings of the 2006 International Conference on Image Processing (ICIP 2006)},
	Note = {(submitted version)},
	Year = {2006},
	Publisher = {ICIP},
	Pages = {1565-1568}

Last modified:: 30.08.2016