Noise reduction of welding defect image based on NSCT and anisotropic diffusion

Abstract

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform (NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation (TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio (PSNR) and mean-square error (MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.

Keywords

welding defect detection / noise reduction / nonsubsampled contourlet transform / total variation model / Catte_PM model

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