NDLI: Fast and accurate bilateral filtering using Gauss-polynomial decomposition

Content Provider	IEEE Xplore Digital Library
Author	Chaudhury, K.N.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India (Chaudhury, K.N.)
Abstract	The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires $O(σ^{2})$ operations per pixel, where σ is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary σ (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.
Starting Page	2005
Ending Page	2009
File Size	1118788
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Polynomials Approximation algorithms Kernel Filtering algorithms Image processing Computer vision fast algorithm Bilateral filter approximation Gauss-polynomial convolution
Content Type	Text
Resource Type	Article

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