基于改进GLU-Net的岩石薄片显微图像拼接OA

Rock thin-section microscopic images frequently exhibit complex local textures,blurriness,and high noise levels,posing significant challenges for traditional feature extraction and matching algorithms.These methods often fail to identify effective feature points in high-resolution rock thin-section images,hindering the realization of panoramic stitching while also resulting in slow processing speeds.To address the aforementioned issues,a rock thin-section microscopic image stitching method based on an improved GLU-Net has been proposed.This method integrates an enhanced correlation computation module to improve global and local correspondence,employs a feature pyramid network for multi-scale feature fusion,incorporates a designed adaptive convolutional attention mechanism to optimize attention to key regions,utilizes global and local decoders to obtain optical flow,and applies homography transformation for image stitching,thereby constructing a novel image stitching network model.Experimental results demonstrate that,compared to traditional image stitching algorithms and other classical image stitching network models,the proposed network achieves superior stitching performance.In stitching tests on a self-constructed dataset,a stitching accuracy of 86.75%has been attained with an average registration time of 0.394 s per pair,effectively balancing enhanced accuracy with processing efficiency.