基于视场分割模型的闪光照相系统图像畸变校正
Image Distortion Correction of Flash Camera Systems Based on Field-of-View Division Model
任宇航 1李斌康 2周二瑞 3杨少华 3严明 3郭明安 3李刚 3刘璐 3王晶 3时明月3
作者信息
- 1. 西安交通大学电气工程学院,陕西 西安 710049
- 2. 西安交通大学电气工程学院,陕西 西安 710049;西北核技术研究所强脉冲辐射环境模拟与效应全国重点实验室,陕西 西安 710024
- 3. 西北核技术研究所强脉冲辐射环境模拟与效应全国重点实验室,陕西 西安 710024
- 折叠
摘要
闪光照相是超高速碰撞、爆炸等瞬态过程的重要诊断技术.闪光照相系统目前多使用光锥耦合的增强型互补金属氧化物半导体(ICMOS)相机作为成像设备,其具有体积小、质量轻、成像速度快等优点.但光锥耦合会给成像过程引入较为复杂的图像畸变,从而降低闪光照相系统的成像性能.本文根据闪光照相系统中ICMOS相机耦合光锥引起的图像畸变特点,提出一种高效的畸变校正方法.基于视场分割模型,针对图像边缘特征点不足引起的边缘校正效果较差问题,对图像边缘进行变步长视场分割,提升图像边缘畸变校正精度.实验结果表明,相较于已有方法,所提方法校正后图像的均方根误差和最大残留误差分别下降17.8%和70.2%.与已有方法相比,所提变步长视场分割法对ICMOS耦合光锥引起的图像畸变具有较高校正精度.
Abstract
Flash photography is an important diagnostic technique for transient processes such as high-speed collisions and explosions.Conventional flash photography systems use enhanced complementary metal-oxide-semiconductor(ICMOS)cameras coupled with light cones as imaging devices,which have the advantages of small size,low weight,and fast imaging speed.However,the coupling of light cones can introduce complex image distortion,thereby reducing the imaging performance.In this study,based on the image distortion characteristics caused by the coupling of light cones in ICMOS cameras employed in flash photography systems,an efficient distortion correction method is proposed.In particular,variable step field-of-view segmentation is performed on image edges based on a field-of-view segmentation model to solve the problem of poor edge correction caused by insufficient edge feature points in images.Experimental results show that compared with existing methods,the proposed method reduces the root mean square error and maximum residual error of the corrected image by 17.8%and 70.2%,respectively.In addition,the proposed variable step field-of-view segmentation method has higher accuracy than existing methods for correcting image distortion caused by ICMOS-coupled light cones.
关键词
图像畸变校正/闪光照相/特征点/视场分割Key words
distortion correction/flash photography/feature points/field-of-view division引用本文复制引用
出版年
2024
NETL
NSTL
万方数据