Since rock fractures have a strong impact on radionuclide transport and retention, fracture edge detection is very important for rock engineering. A number of rock specimens were sampled, and then the rock images were acquired for detecting the fracture edges. Edge detection in color images could be seen as detecting changes in a vector field. The quaternion convolution at different scales is applied. In addition, to suppress image noise, the dot product was computed at adjacent scales. At the same time, the gray-level difference was applied to obtain the monochromatic edges. To merge the merits of the quaternion convolution and the gray-level difference, the two results are combined. In this way, the thinned edges can be obtained by using modulus maximum suppression. Experimental results show that the proposed algorithm is efficient and robust for rock fracture edge detection.
The authors study large panchromatic images and ultraviolet (UV) images. The paper presents an image fusion algorithm based on wavelet transform. Because the panchromatic image contains the detail of rock surface, we select larger low frequency coefficient. At the same time, ultraviolet images contain the important information of rock edge with less detail, so we select larger high frequency coefficient, and use wavelet modulus maximum to find the localizer of UV image edge, use the localizer to enhance the fusion image using panchromatic image. Experiment results show that the fusion algorithm is satisfactory.