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Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion 被引量:4

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion
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摘要 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. 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.
出处 《Transactions of Tianjin University》 EI CAS 2014年第1期60-65,共6页 天津大学学报(英文版)
基金 Supported by the National Natural Science Foundation of China(No.60872065) Open Foundation of State Key Laboratory of Advanced Welding and Connection,Harbin Institute of Technology(AWPT-M04) Priority Academic Program Development of Jiangsu Higher Education Institutions
关键词 welding defect detection noise reduction nonsubsampled contourlet transform total variation model Catte_PM model 各向异性扩散 图像降噪 焊接缺陷 图像噪声 降噪方法 X-射线 电视传播 小波阈值
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