Underground image matching algorithm combining homomorphic filtering and histogram equalization

• Published in: Gong kuang zi dong hua = Industry and mine automation Vol. 47; no. 10; pp. 37 - 41
• Main Authors: GONG Yun, YANG Pangbin, JIE Xinyu
• Format: Journal Article
• Language: Chinese
• Published: Editorial Department of Industry and Mine Automation 01.10.2021
• Subjects: underground image matching; homomorphic filtering; histogram equalization; akaze algorithm; ransac algorithm
• ISSN: 1671-251X
• DOI: 10.13272/j.issn.1671-251x.2021070018

In order to solve the problem of inaccurate feature point extraction and poor matching effect of existing underground image matching algorithms, an underground image matching algorithm combining homomorphic filtering and histogram equalization is proposed. The image is sharpened by homomorphic filtering to improve the image clarity, and the image is processed by the contrast-limited adaptive histogram equalization (CLAHE) algorithm to highlight the edge detail information of the image and improve the image contrast. In order to solve the problem of mis-matching in the traditional AKAZE algorithm, on the basis of rough matching by the brute force matching algorithm, the random sampling consensus (RANSAC) algorithm based on the homography matrix is used to perform accurate matching and eliminate the mis-matched point pairs. The experimental results show that using single-parameter homomorphic filtering and CLAHE algorithm to enhance the image can stretch the gray level of the image, reduce the number of dark pi