DocumentCode :
2338650
Title :
An efficient non-linear algorithm for contrast enhancement of infrared image
Author :
Zhang, Chang-Jiang ; Yang, Fan ; Wang, Xiao-Dong ; Zhang, Hao-Ran
Author_Institution :
Coll. of Inf. Sci. & Eng., Zhejiang Normal Univ., Jinhua, China
Volume :
8
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
4946
Abstract :
An infrared image contrast enhancement algorithm based on a discrete stationary wavelet transform (DSWT) and non-linear gain operator is proposed. Having implemented DSWT on an infrared image, de-noising is done using the method proposed in high frequency sub-bands which have better resolution levels and enhancement is implemented by combining the de-noising method with the non-linear gain method in high frequency sub-bands which have the worse resolution levels. According to experimental results, the new algorithm can reduce effectively the correlative noise (1/f noise), additive Gauss white noise (AGWN) and multiplicative noise (MN) in the infrared image while it also enhances contrast of the infrared image. In terms of visual quality, the algorithm is better than the traditional unshaped mask method (USM), histogram equalization method (HIS) and two methods by Gong et al. (2000) and Wu et al. (2003).
Keywords :
discrete wavelet transforms; image denoising; image enhancement; image resolution; infrared imaging; additive Gauss white noise; correlative noise; discrete stationary wavelet transform; histogram equalization method; image denoising; image resolution; infrared image contrast enhancement algorithm; multiplicative noise; nonlinear algorithm; nonlinear gain operator; unshaped mask method; visual quality; Continuous wavelet transforms; Discrete wavelet transforms; Frequency; Gaussian noise; Histograms; Infrared detectors; Infrared imaging; Machine learning algorithms; Noise reduction; White noise; contrast enhancement; de-noising by threshold; discrete stationary wavelet transform; generalized cross validation; non-linear gain;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527814
Filename :
1527814
Link To Document :
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