数据采集与处理2026，Vol.41Issue(1)：160-173,14.DOI:10.16337/j.1004-9037.2026.01.011
基于改进GLU-Net的岩石薄片显微图像拼接
Image Stitching of Rock Thin Sections Microscopic Images Based on Improved GLU-Net
摘要
Abstract
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.关键词
rock thin-section image/feature fusion/convolutional attention mechanism/optical flow estimation/image stitchingKey words
rock thin-section image/feature fusion/convolutional attention mechanism/optical flow estimation/image stitching分类
信息技术与安全科学引用本文复制引用
向文亮,熊淑华,何海波,滕奇志,何小海..基于改进GLU-Net的岩石薄片显微图像拼接[J].数据采集与处理,2026,41(1):160-173,14.基金项目
国家自然科学基金(62071315) （62071315）
四川省国际科技创新合作项目(2024YFHZ0289). National Natural Science Foundation of China(No.62071315) （2024YFHZ0289）
Sichuan Provincial International Science and Tech-nology Innovation Cooperation Project(No.2024YFHZ0289). （No.2024YFHZ0289）
