基于灰度线性建模的亚像素图像抖动量计算

智喜洋; 侯晴宇; 王少游

doi:10.3788/OPE.20162401.0195

您当前的位置：

首页 >

文章列表页 >

基于灰度线性建模的亚像素图像抖动量计算

信息科学 | 更新时间：2020-08-13

- 基于灰度线性建模的亚像素图像抖动量计算
- Estimation of image sub-pixel jitter based on linear model of image gray level
- 光学精密工程 2016年24卷第1期页码：195-202
- 作者机构：
  
  1. 哈尔滨工业大学空间光学工程研究中心,黑龙江哈尔滨,150001
  2. 上海卫星工程研究所, 上海 200240
- 作者简介：
- 基金信息：
  
  中国航天科技集团公司航天科技创新基金资助项目()
- DOI：10.3788/OPE.20162401.0195
  中图分类号： TP391;TP751
- 收稿日期：2015-08-18，
  
  修回日期：2015-10-12，
  
  纸质出版日期：2016-01-25
- 稿件说明：
移动端阅览
智喜洋, 侯晴宇, 王少游. 基于灰度线性建模的亚像素图像抖动量计算[J]. 光学精密工程, 2016,24(1): 195-202

ZHI Xi-yang, HOU Qing-yu, WANG Shao-You. Estimation of image sub-pixel jitter based on linear model of image gray level[J]. Editorial Office of Optics and Precision Engineering, 2016,24(1): 195-202
智喜洋, 侯晴宇, 王少游. 基于灰度线性建模的亚像素图像抖动量计算[J]. 光学精密工程, 2016,24(1): 195-202 DOI： 10.3788/OPE.20162401.0195.

ZHI Xi-yang, HOU Qing-yu, WANG Shao-You. Estimation of image sub-pixel jitter based on linear model of image gray level[J]. Editorial Office of Optics and Precision Engineering, 2016,24(1): 195-202 DOI： 10.3788/OPE.20162401.0195.

摘要

为了解决凝视遥感云场景序列图像的亚像素抖动量求解问题

提出了基于灰度线性建模的亚像素序列图像抖动量计算方法。首先

利用三参数线性模型描述像素及邻域灰度

提出了一种图像灰度的线性建模方法。其次

以序列帧图像相对参考帧图像的抖动量作为线性模型中的优化变量

以参考帧图像与序列帧图像之间的相似性为优化目标

建立了亚像素抖动量解算的最小二乘优化方法

并推导得到了解析计算公式。最后

利用云场景序列图像进行了算法仿真验证。结果表明

该方法抖动量的计算误差小于0.1 pixel。将该方法与传统基于特征点的配准算法进行了比较

结果显示该方法具有较高的抖动量计算精度

可应用于遥感图像几何定标、目标定位以及时序图像中目标多帧关联检测等。

Abstract

A new method based on the linear model of image gray level was proposed to estimate sub-pixel jitters of a sequence image for cloud scene acquired by staring remote sensing imaging systems. Firstly

the image pixel and corresponding neighborhood gray levels were mathematically described by using linear model with three parameters

and the image gray was modeled. Then

by taking the jitter parameter as optimization variables in the linear mathematic model and the comparability between image sequences and reference frame as optimization objective

a new estimation method for the sub-pixel jitter was proposed based on least square optimization approach. Subsequently

the solving equation was derived. Finally

the method was verified by using simulated image sequences containing the cloud scene. Experimental results indicate that the proposed method is able to implement the subpixel estimation effectively and offers the estimation accuracy no less than 0.1 pixel. The obtained estimation accuracy is higher than that of the conventional feature-based ones

and can be used in geometric calibration and target positioning of remote sensing images as well the multi-frame relative detection of time-series images.

关键词

Keywords

references

PARIS C. Vibration tests on the preloaded LARES satellite and separation system [J]. Aerospace Science and Technology, 2015, 42: 470-476.

ZHI X Y, HOU Q Y, SUN X, et al.. Degradation and restoration of high resolution TDICCD imagery due to satellite vibrations [C]. International Symposium on Optoelectronic Technology and Application 2014,International Society for Optics and Photonics, 2014: 93012I-1-93012I-8.

LIN CH, LIN B Y, LEE K Y, et al.. Radiometric normalization and cloud detection of optical satellite images using invariant pixels [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106: 107-117.

ZHU Z, WANG S X, WOODCOCK C E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images [J]. Remote Sensing of Environment, 2015, 159: 269-277.

JAYADEVAN V T, RODRIGUEZ J J, CRONIN A D. A new contrast-enhancing feature for cloud detection in ground-based sky images [J]. Journal of Atmospheric and Oceanic Technology, 2015, 32(2): 209-219.

聂宏宾, 侯晴宇, 赵明, 等. 基于似然函数EM迭代的红外与可见光图像配准[J]. 光学精密工程, 2011, 19(3): 657-663. NIE H B, HOU Q Y, ZHAO M, et al.. IR/visible image registration based on EM iteration of log-likelihood function [J]. Opt. Precision Eng., 2011, 19(3):658-663.(in Chinese)

WU T J, GE Y, WANG J, et al.. A WTLS-based method for remote sensing imagery registration [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 102-116.

WU Y, MA W, GONG M, et al.. A novel point-matching algorithm based on fast sample consensus for image registration [J]. Geoscience and Remote Sensing Letters, IEEE, 2015, 12(1): 43-47.

林培杰, 郑柏春, 陈志聪,等. 面向多区域视频监控的运动目标检测系统[J]. 液晶与显示, 2015, 30(3):484-491. LIN P J, ZHENG B CH, CHEN ZH C, et al.. System of multi-regions moving object detection in video surveillance [J]. Chinese Journal of Liquid Crystals and Displays, 2015, 30(3):484-491.(in Chinese)

KUPFER B, NETANYAHU N S, SHIMSHONI I. An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images [J]. Geoscience and Remote Sensing Letters, IEEE, 2015, 12(2): 379-383.

JIANG J, ZHANG S, CAO S. Rotation and scale invariant shape context registration for remote sensing images with background variations [J]. Journal of Applied Remote Sensing, 2015, 9(1): 095092-1-095092-20.

TENG S W, HOSSAIN M T, LU G. Multimodal image registration technique based on improved local feature descriptors [J]. Journal of Electronic Imaging, 2015, 24(1): 013013-1-013013-17.

WANG Z, KIEU H, NGUYEN H, et al.. Digital image correlation in experimental mechanics and image registration in computer vision: Similarities, differences and complements [J]. Optics and Lasers in Engineering, 2015, 65: 18-27.

王洪涛, 李丹. 基于改进粒子群算法的图像边缘检测研究[J]. 液晶与显示, 2014, 29(5):800-804. WANG H T, LI D. Image edge detection research based on improved particle swarm optimization algorithm [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(5):800-804.(in Chinese)

李志军, 刘松林, 牛照东,等. 基于梯度相位和显著性约束的Hausdorff距离模板匹配方法[J]. 红外与激光工程, 2015, 44(2):775-780. LI ZH J, LIU S L, NIU ZH D, et al.. Hausdorff distance template matching method based on gradient phase and significance constraints [J]. Infrared and Laser Engineering, 2015, 44(2):775-780.(in Chinese)

赵爱罡, 王宏力, 杨小冈,等. 融合几何特征的压缩感知SIFT描述子[J]. 红外与激光工程, 2015, 44(3):1085-1091. ZHAO A G, WANG H L, YANG X G, et al.. Compressed sense SIFT descriptor mixed with geometrical feature [J]. Infrared and Laser Engineering, 2015, 44(3):1085-1091.(in Chinese)

GHAFURIAN S, HACIHALILOGLUB I, METAXAS D N, et al.. 3D/2D image registration using weighted histogram of gradient directions [C]. SPIE Medical Imaging, International Society for Optics and Photonics, 2015: 94151Z-1-94151Z-7.

AGANJ I, REUTER M, SABUNCU M R, et al.. Avoiding symmetry-breaking spatial non-uniformity in deformable image registration via a quasi-volume-preserving constraint [J]. NeuroImage, 2015, 106: 238-251.

DURAISAMY P, YOUSEF A, BUCKLES B, et al.. Image registration under symmetric conditions: novel approach [C]. Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770N, 2015: 1-5.

LEE I H, MAHMOOD M T. Robust registration of cloudy satellite images using two-step segmentation[J]. Geoscience and Remote Sensing Letters, IEEE, 2015, 12(5): 1121-1125.

LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

浏览量

604

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向单幅遥感图像超分辨率的空间自适应及频率融合网络

融合边缘增强与非局部模块的遥感图像超分辨率重建生成对抗网络

融入一维概率Hough变换与局部Zernike矩的双目视觉测量

融合CNN和Transformer的遥感图像建筑物快速提取

融合分形几何特征Resnet遥感图像建筑物分割