Impulsive noise filter using median- and partition-based operation

ChangCheng Wu; DaYue Chen; ChunYu Zhao

doi:10.1117/1.3028332

1 November 2008 Impulsive noise filter using median- and partition-based operation

ChangCheng Wu, DaYue Chen, ChunYu Zhao

Author Affiliations +

Optical Engineering, Vol. 47, Issue 11, 117004 (November 2008). https://doi.org/10.1117/1.3028332

Abstract

A novel filter is proposed in this work to remove noise and preserve fine details from the impulse-corrupted images based on median and partition operation. Each pixel is classified into one of the mutually exclusive partition blocks by a novel observation vector. This observation vector can be directly used to partition the observation space without using predefined thresholds. The weight of each block is obtained using a learning approach based on the least-mean-square algorithm. The noise-filtering process is progressively applied through several iterations to achieve better noise-suppression results. Several experimental results have demonstrated that the proposed filter outperforms many filters in terms of both noise suppression and detail preservation.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

ChangCheng Wu, DaYue Chen, and ChunYu Zhao "Impulsive noise filter using median- and partition-based operation," Optical Engineering 47(11), 117004 (1 November 2008). https://doi.org/10.1117/1.3028332

Published: 1 November 2008

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available