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

Mahapatra, P.K. and Ganguli, Susmita and Kumar, Amod (2015) A hybrid particle swarm optimization and artificial immune system algorithm for image enhancement. Soft Computing, 19 (8). 2101-2109 . ISSN 1432-7643

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Official URL: http://link.springer.com/article/10.1007%2Fs00500-...


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.

Item Type: Article
Uncontrolled Keywords: Particle swarm optimization, Artificial immune system, Negative selection algorithm, Image enhancement, Histogram equalization, Linear contrast stretching
Subjects: CSIO > Computational Instrumentation
CSIO > Medical Instrumentation
Divisions: Computational Instrumentation
Depositing User: Ms. Jyotsana
Date Deposited: 09 Aug 2018 12:08
Last Modified: 09 Aug 2018 12:08
URI: http://csioir.csio.res.in/id/eprint/586

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