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Journal of Information Science and Engineering, Vol. 22 No. 4, pp. 925-939 (July 2006)

A HVS-Directed Neural-Network-Based Approach for Salt-Pepper Impulse Noise Removal

Shih-Mao Lu1, Sheng-Fu Liang and Chin-Teng Lin1,2
1Department of Electrical and Control Engineering
2Department of Computer Science
Department of Biological Science and Technology
National Chiao Tung University
Hsinchu, 300 Taiwan

In this paper, a novel two-stage noise removal algorithm to deal with salt-pepper impulse noise is proposed. In the first stage, the decision-based recursive adaptive noise- exclusive median filter is applied to remove the noise cleanly and to keep the uncor-rupted information as well as possible. In the second stage, the fuzzy decision rules in-spired by human visual system (HVS) are proposed to classify image pixels into human perception sensitive class and non-sensitive class. A neural network is proposed to com-pensate the sensitive regions for image quality enhancement. According to the experi-mental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions of the resultant images.

Keywords: salt-pepper, impulse noise, noise removal, fuzzy decision system, human visual system, neural network

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Received August 17, 2004; revised February 15 & June 17, 2005; accepted July 27, 2005.
Communicated by Liang-Gee Chen.