Underground image matching algorithm combining homomorphic filtering and histogram equalizatio

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 pixels and increase the number of bright pixels, which makes the gray level distribution smoother and helps to preserve the details and boundary information of the image. Using RANSAC algorithm based on the homography matrix for accurate matching can detect more feature points and improve the matching accuracy. The matching effect is better than that of SURF algorithm and traditional AKAZE algorithm with a maximum of 96.09%.