To address the issues of uneven ORB feature point extraction, slow matching speed, and low matching accuracy in traditional image processing, this paper proposes an improved image processing algorithm for ORB feature extraction and matching. First, an enhanced quadtree algorithm is employed to achieve uniform extraction of ORB feature points, maximizing the utilization of information across the entire image. Second, brute-force matching combined with GMS screening is implemented to preliminarily filter feature matches, thereby enhancing matching accuracy. Finally, a dynamic Bayesian network is utilized to select the optimal matching model and best-matched pairs, further improving matching accuracy while reducing screening time. Experimental results demonstrate that compared with traditional algorithms, the proposed method significantly improves feature distribution uniformity. The average time required for feature extraction and matching is reduced compared to other improved algorithms, while achieving higher matching accuracy—specifically showing a 49.1% improvement over conventional ORB algorithms. The overall performance surpasses both traditional ORB and other existing improved algorithms. It is demonstrated that the image processing based on improve ORB feature extraction and matching can effectively achieve simultaneous improvements in both feature matching accuracy and feature matching speed.