Abstract
Having performed discrete stationary wavelet transform (DSWT) to an infrared image and employing generalized cross validation (GCV), noise is reduced in high frequency sub-bands. These sub-bands are at finer resolution levels and the local contrast is enhanced by combining the de-noising method with non-linear operator in these sub-bands which are at coarser resolutionn levels. In order to enhance global contrast for an infrared image, the low frequency sub-band image is also enhanced employing non-incomplete Beta transform (IBT), simulated annealing algorithm (SA) and wavelet neural network (WNN). IBT is used to obtain non-linear gray transform curve. Transform parameters are determined by SA so as to obtain optimal non-linear gray transform parameters. Contrast type of an original image is determined by a new criterion. Gray transform parameters space is determined respectively according to different contrast types. A kind of WNN is proposed to approximate the IBT in the whole low frequency sub-band image. The quality of enhanced image is evaluated by an overall cost criterion. Experimental results show that the new algorithm can greatly improve the global and local contrast for an infrared image while efficiently reducing the correlative noise (CN), gauss white noise (GWN) and multiplicative noise (MN) in the infrared image.
Published Version
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