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《Journal of the China Railway Society》 2015-02
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Using Adaptive Multiscale Products Thresholding to Enhance the Infrared Image of a Rail Crack

GU Gui-mei;HUANG Tao;School of Automation and Electrical Engineering,Lanzhou Jiaotong University;  
The infrared image obtained from the use of infrared thermal wave technology to detect an oblique crack of a rail has a number of shortcomings,including a low overall brightness,low edge contrast and blurred minutiae texture,which would result in detrimental impact on the measurement of the depth of the crack expansion.Therefore in this paper,a nonsubsampled Contourlet transform(NSCT)based adaptive multiscale products thresholding(AMPT)algorithm is proposed to enhance the infrared image of a rail crack.Firstly,the image was decomposed using the NSCT to obtain multi-scale and multi-direction sub-band coefficients,which then adaptively selected the threshold values and adjusted the enhancement function;subsequently the threshold was applied to the multi-scale product of each sub-band;and finally the thresholding processed sub-band coefficients were enhanced and the results underwent a further inverse transformation,thereby realizing the image enhancement.The results of the experiment demonstrate that the proposed algorithm can suppress the noise,increase the overall image contrast,while effectively highlight the edge of the target image.This approach is superior in inner-crack minutiae texture enhancement to traditional infrared image enhancing methods,therefore providing apowerful support for the further extraction of key pixels that can characterize the depth of a crack.
【CateGory Index】: TP391.41
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