An industrial CT image adaptive filtering method based on anisotropic diffusion

Abstract

Industrial CT, by which the internal structures of industrial components are examined without destruction, is an useful tool in construction engineering and manufacturing. However, lot of artifacts and noise are usually generated in the industrial CT image and troubles are brought synchronously on the visualization and classification of industrial CT volume data. The goal of our work is to decrease the noise and artifacts in the industrial CT image by anisotropic diffusion. Anisotropic diffusion algorithms which can keep important edges sharp and spatially fixed while filtering noise and small edges, remove noise from an image by modifying the image via a partial differential equation. In conventional anisotropic diffusions resulting in the loss of image details and cause false contours, 4-neighborhood directions are used usually except diagonal directions of the image. To overcome the shortcoming of these conventional anisotropic diffusion methods, a new anisotropic diffusion method for industrial CT image based on the types of gradient directions is proposed in this paper. In our work, one parameter K is computed first by the histogram of the gradient. Then Sobel operator is used to calculate the directions of gradient. The directions of the gradient are classified. Experiments results show that the proposed algorithm can remove noise and artifacts from industrial CT volume data sets is better than the Gaussian filter and other traditional algorithm. In the future, we will engage in reducing time-cost of this diffusion algorithm in 3D filtering.