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

被引：25
|
作者：
Mahapatra, Prasant Kumar [1 ]
Ganguli, Susmita [1 ,2 ]
Kumar, Amod [1 ]
机构：
[1] CSIR, Cent Sci Instruments Org, V Biomed Instrumentat Div 2, Chandigarh 160030, India
[2] Tezpur Univ, Dept Elect & Commun, Tezpur 784028, India
来源：
SOFT COMPUTING | 2015年 / 19卷 / 08期
关键词：
Particle swarm optimization; Artificial immune system; Negative selection algorithm; Image enhancement; Histogram equalization; Linear contrast stretching; CONTRAST ENHANCEMENT; FITNESS ESTIMATION; PSO;
D O I：
10.1007/s00500-014-1394-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2101 / 2109
页数：9
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