Abstract:

The infrared thermal wave image in the nondestructive testing have the disadvantages of low contrast, blurred edges and strong noise. Thus an adaptive enhancement method of the infrared thermal wave image based on contourlet transform and adaptive chaotic variation particle swarm optimization (ACPSO) is proposed. An infrared thermal wave image is decomposed into a low-pass subband and band-pass directional subbands through the contourlet transform. Then the coefficients of lowpass subband are adjusted according to a grayscale transform, which is adapted to the human visual system. The related parameters are determined by ACPSO. In order to obtain the best enhancement effect, the fitness function can measure the contrast of images. While the coefficients of bandpass directional subbands are adjusted by a nonlinear gain function. Thus noise is suppressed and details are enhanced. A large number of experimental results of infrared thermal wave image enhancement show that, compared with four existing image enhancement methods, the proposed method can improve the contrast between the defects and the background greatly, enhance defect edges and suppress noise. While multithresholding method using maximum reciprocal entropy is further adopted, the defects are exacted more efficiently. The proposed method lays the foundation for the subsequent accurate defect recognition and measurement of defect sizes.