Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

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 anisot-ropic 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, respec-tively. 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 diffu-sion, 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.