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Parameter optimisation and rule base selection for fuzzy impulse filters using evolutionary algorithms

conference contribution
posted on 06.12.2017, 00:00 authored by Mohamed AnverMohamed Anver, Russel StonierRussel Stonier
In this paper we present an effective scheme for impulse noise removal from highly corrupted images using a soft-computing approach, The filter is capable of preserving the intricate details of the image and is based on a combination of fuzzy impulse detection and restoration of corrupted pixels. In the first stage a fuzzy knowledge base required for detection of impulses as well as the optimum parameters for the fuzzy membership functions employed, are effectively 'learnt" using an Evolutionary Algorithm (EA). For the detection of noisy pixels and the subsequent replacement, a novel scheme where a pixel is transferred to a simulated noise free environment is introduced. We present the results for several real images and make comparisons with some of the existing noise removal methods wherever applicable to show the effectiveness of the proposed technique.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

31

End Page

35

Number of Pages

5

Start Date

01/01/2003

Finish Date

01/01/2003

ISBN-10

0780381629

Location

Bangalore, India

Publisher

Allied Publishers

Place of Publication

New Delhi, India

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Informatics and Communication;

Era Eligible

No

Name of Conference

TENCON