煤矿安全2026，Vol.57Issue(5)：247-257,11.DOI:10.13347/j.cnki.mkaq.20250792
基于光流与稀疏注意力的红外视频去运动模糊算法
Infrared video motion deblurring algorithm based on optical flow and sparse attention
摘要
Abstract
The underground coal mine working environment is characterized by complex spatial structures and significant factors such as widespread equipment vibration.These unique conditions pose severe challenges to the quality of infrared video.Among them,the issues of intra-frame and inter-frame motion blur caused by rapid target movement and the inherent vibration of under-ground equipment are particularly prominent.Such blurring leads to the loss of image texture,unclear target contours,and degrada-tion of details,thereby reducing the accuracy and effectiveness of subsequent visual processing tasks.To address these challenges,this paper proposes a Transformer-based infrared video deblurring algorithm guided by optical flow with sparse attention.First,op-tical flow information is utilized to guide the computation of the sparse attention matrix in the Transformer.A hierarchical sparse at-tention mechanism is applied to adaptively focus on low-contrast key regions,separating infrared features from motion blur compon-ents.Simultaneously,recurrent spatiotemporal dependency modeling is introduced to capture and leverage motion information and feature continuity across adjacent frames,addressing the issue of insufficient contextual information in single-frame deblurring.Second,the optical flow estimation is refined through a global motion aggregation module.This module accurately infers and cor-rects optical flow vectors in occluded areas based on surrounding reliable optical flow information and contextual semantic features,overcoming the limitations of traditional optical flow algorithms in occluded regions,thereby providing more accurate and reliable motion information for the subsequent deblurring process.Finally,a motion blur enhancement method based on physical trajectory modeling is employed to construct a coal mine rock roadway drilling scene dataset with heavy coal dust fog dataset(CMRRD-HCDF,this dataset covers various operational states,interference levels,and complex occlusion scenarios)tailored for heavy dust and fog conditions in coal mine to evaluate the algorithm's generalization capability in real underground coal mine environments.Experimental results show that the algorithm achieves a peak signal-to-noise ratio(PSNR)of 29.508 2 dB and a structural SIMilarity index(SSIM)of 0.889 3 on the uncooled infrared image deblurring dataset(UIRD),and a PSNR of 33.136 7 dB and an SSIM of 0.958 6 on the CMRRD-HCDF dataset.关键词
图像智能检测/深度学习/计算机视觉/红外图像/去运动模糊/光流估计Key words
intelligent image detection/deep learning/computer vision/infrared image/motion deblurring/optical flow estimation分类
矿业与冶金引用本文复制引用
白天昕,冯文彬,于重重,谢涛,郑彤..基于光流与稀疏注意力的红外视频去运动模糊算法[J].煤矿安全,2026,57(5):247-257,11.基金项目
"十四五"国家重点研发计划资助项目(2023YFB3211003) （2023YFB3211003）
北京市自然科学基金面上资助项目(4252031) （4252031）
