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Proceedings Paper

Adaptive neighborhood filters for color image filtering
Author(s): Mihai Ciuc; Rangaraj M. Rangayyan; Titus Zaharia; Vasile V. Buzuloiu
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Paper Abstract

Various nonlinear, fixed-neighborhood techniques for filtering color images based on local statistics have been proposed in the literature. We present adaptive neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel in the image (called the 'seed' when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed using pixels within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double- window modified trimmed-mean filter, the generalized vector directional filter -- double-window -- (alpha) -trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive non-parametric filter with Gaussian kernel. It is shown that the ANF techniques provide the best visual results, effectively suppressing noise while not blurring edges. The ANF results are also the best in terms of objective measures (such as normalized mean-squared error and normalized color difference).

Paper Details

Date Published: 6 April 1998
PDF: 10 pages
Proc. SPIE 3304, Nonlinear Image Processing IX, (6 April 1998); doi: 10.1117/12.304609
Show Author Affiliations
Mihai Ciuc, Univ. of Calgary and Univ. Politehnica Buchuresti (Romania)
Rangaraj M. Rangayyan, Univ. of Calgary (Canada)
Titus Zaharia, Univ. Politehnica Buchuresti (France)
Vasile V. Buzuloiu, Univ. Politehnica Buchuresti (Romania)


Published in SPIE Proceedings Vol. 3304:
Nonlinear Image Processing IX
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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