Brunel University Research Archive (BURA) >
Schools >
School of Information Systems, Computing and Mathematics >
School of Information Systems, Computing and Mathematics Research Papers >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4887

Title: Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images
Authors: Xu, X
Miller, E L
Chen, D
Sarhadi, M
Keywords: Center-weighted median filter (CWMF)
Error index matrix
Impulse noise
Lower-upper-middle (LUM) filter
Median filter
SD-ROM filter
Spatial distribution of impulse noise
Publication Date: 2004
Publisher: IEEE
Citation: IEEE Transactions on Image Processing 13(2): 238-247, Feb 2004
Abstract: In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the sec ond filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.
Description: This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. © 2004 IEEE.
Sponsorship: This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (NSF) under Award EEC-9986821, by an ARO MURI on Demining under Grant DAAG55-97-1-0013, and by the NSF under Award 0208548.
URI: http://bura.brunel.ac.uk/handle/2438/4887
DOI: http://dx.doi.org/10.1109/tip.2004.823827
ISSN: 1057-7149
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

Files in This Item:

File Description SizeFormat
Fulltext.pdf927.45 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan