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Proceedings Paper

Image noise smoothing based on nonparametric statistics
Author(s): Keh-Shih Chuang; H. K. Huang
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Paper Abstract

In this paper we describe a novel noise smoothing method based on a nonparametric statistic runs test. We assume that the data bits of a pixel can be divided into signal bits and noise bits. The signal comprises the most significant bits and the noise bits are the least significant ones. The idea in this smoothing method is to preserve the signal bits and only modify the noise bits. The number of noise bits of each pixel is determined based on the runs in the neighborhood. If the number of noise bits is zero then no smoothing is necessary. The degree of smoothing is a function of the number of noise bits. Using this technique we are able to smooth only noisy areas without reducing the spatial resolution in the image. The algorithm is easy to implement. The application of the smoothing algorithm on a chest image was given.

Paper Details

Date Published: 1 June 1991
PDF: 8 pages
Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); doi: 10.1117/12.45246
Show Author Affiliations
Keh-Shih Chuang, UCLA School of Medicine (United States)
H. K. Huang, UCLA School of Medicine (United States)


Published in SPIE Proceedings Vol. 1445:
Medical Imaging V: Image Processing
Murray H. Loew, Editor(s)

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