Abstract: Generating panoramic borehole wall images and "virtual cores" through image unwrapping and stitching is the basis for quantitative analysis of geological structures and is also an urgent need in borehole investigation. To address the problems of blurred image contours and discontinuous stitching in existing borehole image unwrapping and stitching methods, an image unwrapping method based on the Random Sample Consensus (RANSAC) algorithm and an image stitching method based on the Speeded Up Robust Features (SURF) plus Maximum Likelihood Estimation Sample Consensus (MLESAC) algorithm were proposed. The RANSAC algorithm was used to perform circle fitting on borehole edge data to obtain the borehole center and borehole inner diameter, thereby determining the effective annular image region. By combining the coordinate transformation algorithm and bilinear interpolation method, the effective annular region was then unwrapped into a rectangular image, which effectively compensated for geometric distortion caused by probe jitter and achieved high-precision unwrapping of borehole images. The SURF algorithm was used to rapidly extract feature points of adjacent images and perform coarse matching while maintaining feature stability under image rotation and scale transformation. The MLESAC algorithm was then used to perform precise matching of the extracted feature points, remove mismatched points, and select the optimal matching pairs, thereby estimating horizontal and vertical offset parameters and achieving global high-precision stitching of video images. Comparative analysis results showed that, compared with the image unwrapping and stitching methods used in existing devices, the proposed method produced panoramic borehole wall images that were continuous and uninterrupted, with high-resolution detail, and significantly improved both stitching quality and visual quality.