Pore network extraction is mostly used for constructing digital cores and computing complex rock properties. However, this method strongly depends on the quality of CT-scanning, which can vary due to several factors (e.g., low X-Ray radiation exposure time). The segmentation of these images needs several processing steps to obtain a segmented image.

In this study, de-noising filters such as Non-local means, Gaussian, and Median filters are applied to the acquired CT-images to reduce noise. The wave transformation equation is used to extract a homogeneous image. To remove the shadow effect from images, we used the Sobel algorithm to detect edge pixels first and assign them to pore or grain phases based on a threshold value. All codes are implemented in Python.

Applying the above filters to a micro CT-image of a carbonate rock sample shows that the proposed method successfully decreased the influence of low X-Ray radiation time. Among the applied filters, the Gaussian filter provides acceptable results during the noise removal process. Implementing the wave transform equation makes the CT images homogeneous enough that they could be segmented by the simple Otsu thresholding. The porosity of the extracted network is also validated with that of the conventional methods.


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