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.

To suppress noise of image more efficiently and further improve image visual effects, a noise suppression method of image based on shearlet transform and kernel anisotropic diffusion is proposed. Firstly, a noisy image is decomposed by nonsubsampled shearlet transform(NSST). Then the obtained low?frequency component and high?frequency components are processed by improved total variation ( ITV) diffusion and kernel anisotropic diffusion (KAD), respectively. Finally, the noise suppressed image is obtained by synthesizing diffused low?frequency component and high?frequency components through inverse nonsubsampled shearlet transform(INSST). Experimental results are given, in terms of subjective visual effect and two quantitative evaluation indicators such as peak signal to noise ratio (PSNR), structural similarity (SSIM), a comparison is made with three recent proposed noise suppression methods based on wavelet threshold shrinkage and TV, based on nonlinear diffusion in complex contourlet domain, and using TV with adaptive shearlet domain restraint. A large number of experimental results show that the proposed method has stronger noise suppression ability and preserves edge and detail information more completely.

为了有效抑制数字全息再现像的散斑噪声,进一步改善再现像的像质,提出了一种基于双树复小波变换(DT-CWT)和各向异性扩散的数字全息再现像散斑噪声抑制方法.将再现像进行双树复小波分解,对低频分量和6个方向的高频分量分别采用改进的P_Laplace扩散和拉普拉斯金字塔非线性扩散(LPND),通过双树复小波逆变换(IDT-CWT)重构再现像.给出了实验结果,并与小波阈值收缩和全变差(TV)扩散方法、拉普拉斯金字塔非线性扩散的方法、Contourlet结合TV扩散和自适应对比度扩散的方法进行了主观视觉比较,同时依据峰值信噪比(PSNR)、相关系数(COR)及运行时间等进行了客观定量评价.结果表明,本方法对散斑噪声抑制能力更强,并能更好地保留再现像的细节纹理特征.

Image noise reduction has been identified as the first step of automatic fabric defect detection.Its effect has a direct impact on subsequent image segmentation,feature extraction and the final recognition result.To improve the performance of noise reduction,a method of noise reduction for a fabric defect image based on complex contourlet transform and anisotropic diffusion has been proposed.Firstly,a fabric defect image was decomposed into low-frequency and high-frequency components through complex contourlet transform.Next,a P_Laplace operator and Catte_PM model were used to diffuse low-frequency and high-frequency components,respectively.Finally,the defect image was reconstructed by inverse complex contourlet transform.A large number of experimental results indicate that,compared with the hybrid method of wavelet threshold shrinkage with total variation diffusion,the method combining the wavelet with PM model diffusion,the method combining contourlet with total variation and adaptive contrast diffusion,and the method combining nonsubsampled contourlet with nonlinear diffusion,the proposed method has great improvement in both subjective visual effect and objective quantitative evaluation indicators,which can preserve the texture and details of fabric image better.The proposed method has stronger noise reduction capabilities and can suppress noise effectively.

In order to improve the accuracy and computational efficiency of change detection of multi-temporal remote sensing images, a change detection algorithm based on nonsubsampled contourlet transform (NSCT) and independent component analysis (ICA) is proposed. The flexibility of NSCT in image decomposition and the effectiveness of ICA in image separation are used comprehensively. Firstly, multi-scale decomposition of remote sensing images is performed by NSCT. Then the decomposed low-frequency components and high-frequency components form into partitioned vectors. ICA is carried out for the partitioned vectors and separates mutual independent components. Next the separated components are transformed into image components which include the change image. Finally, change detection result is achieved by threshold segmentation and filtering of the change image. The experimental results show that, compared with the algorithm based on ICA, the algorithm based on wavelet transform and ICA, the proposed algorithm separates change information more effectively and reduces computational complexity. The obtained change image has higher accuracy and stronger robustness to the background.

Aiming at welding defect image with complex background and low contrast, a segmentation method of welding defect image based on exponential cross entropy and improved pulse coupled neural network (PCNN) is proposed. Firstly, the area of weld is extracted by gray projection algorithm. Then, link weighted matrix and dynamic threshold function of PCNN are improved. Finally, the exponential cross entropy is calculated as criterion to determine the number of iteration for improved PCNN and get the optimal segmented image. The experimental results are given. Compared with the threshold segmentation method based on exponential cross entropy, the segmentation method based on PCNN and Shannon entropy, the proposed method can achieve better segmented results.

Segmentation of defect images is an important step in the automatic fabric defect detection. In order to extract fabric defects effectively, a segmentation method of fabric defect images based on pulse coupled neural network (PCNN) model and symmetric Tsallis cross entropy is proposed. The image is segmented by PCNN according to the gray strength difference between fabric defect area and non-defect area. To guarantee that the grayscale inside the object and background is uniform after segmentation, symmetric Tsallis cross entropy is used as the image segmentation criterion to select the optimal threshold and iteration number. A large number of experimental results show that, compared with the related segmentation methods such as Otsu method, PCNN method, the method based on PCNN and cross entropy, the segmentation effect of the proposed method is the best. The texture of non-defect area is removed more completely, and the defect area is segmented more accurately.