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Soft Computing

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01.08.2015 | Methodologies and Application

A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement

verfasst von: Prasant Kumar Mahapatra, Susmita Ganguli, Amod Kumar

Erschienen in: Soft Computing | Ausgabe 8/2015

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Abstract

Image enhancement means to improve the perception of information in images. Histogram equalization (HE) and linear contrast stretching (LCS) are the commonly used methods for image enhancement. But images obtained through these processes, generally, have excessive contrast enhancement due to which they are not suitable for use in fields where brightness is of critical importance. In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) along with Negative Selection Algorithm, a model of artificial immune system, is proposed for image enhancement which is achieved by enhancing the intensity of the gray levels of the images. The proposed algorithm is applied to histogram equalized images of lathe tool and MATLAB inbuilt images to verify its effectiveness. The results are compared with conventional enhancement techniques such as HE, LCS and Standard PSO algorithm based image enhancement.

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Titel: A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement
verfasst von: Prasant Kumar Mahapatra
Susmita Ganguli
Amod Kumar
Publikationsdatum: 01.08.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 8/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1394-6

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