Abstract
In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.
Highlights
Due to the influence of equipment and acquisition environment in X-ray welding image acquisition, welding images are limited by low contrast and large noise, which will interfere with the segmentation and recognition of welding image defects
We compared various denoising methods based on anisotropic diffusion, and evaluated their performances with mean square error (MSE) and peak signal-to-noise ratio (PSNR), which underlay further image segmentation and feature extraction experiments on noisy weld X-ray images [5]
Based on the above analyses and in view of the Gaussian and salt-and-pepper mixed noise in weld images, firstly noise separation was carried out, an improved nonlocal mean filter was proposed to reduce the Gaussian noise and an adaptive Takagi-Sugeno (T-S) fuzzy fusion filter algorithm which can identify the probability of salt and pepper noise was proposed in combination with the median filter
Summary
Due to the influence of equipment and acquisition environment in X-ray welding image acquisition, welding images are limited by low contrast and large noise, which will interfere with the segmentation and recognition of welding image defects. A local image enhancement method was proposed by determining the local pixel nonuniformity factor, and the combination of histogram equalization and contrast limitation can adaptively improve the defect detection accuracy [3]. We compared various denoising methods based on anisotropic diffusion, and evaluated their performances with mean square error (MSE) and peak signal-to-noise ratio (PSNR), which underlay further image segmentation and feature extraction experiments on noisy weld X-ray images [5]. Based on the above analyses and in view of the Gaussian and salt-and-pepper mixed noise in weld images, firstly noise separation was carried out, an improved nonlocal mean filter was proposed to reduce the Gaussian noise and an adaptive Takagi-Sugeno (T-S) fuzzy fusion filter algorithm which can identify the probability of salt and pepper noise was proposed in combination with the median filter. Experiments show that the proposed algorithm has obvious advantages in noise reduction and enhancement
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
