地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (1): 101-108,122.doi: 10.3724/SP.J.1047.2012.00101

• 遥感技术与应用 • 上一篇    下一篇

一种以CCD拼接结构的航空相机智能调焦方法

李珊珊1,2,3,4, 宫辉力1,2,3,4, 刘杨5*, 胡卓玮1,2,3,4   

  1. 1. 首都师范大学资源环境与旅游学院, 北京 100048;
    2. 资源环境与地理信息系统北京市重点实验室, 北京 100048;
    3. 三维信息获取与应用教育部重点实验室, 北京 100048;
    4. 灾害评估与风险防范民政部重点实验室, 北京 100048;
    5. 哈尔滨工业大学航天学院, 哈尔滨 150001
  • 收稿日期:2011-09-12 修回日期:2012-02-02 出版日期:2012-02-25 发布日期:2012-02-24
  • 通讯作者: 刘 杨(1982-),男,天津人,博士,主要从事信号处理和故障诊断方面的研究。E-mail:light520@yahoo.com.cn E-mail:light520@yahoo.com.cn
  • 作者简介:李珊珊(1983-),女,博士研究生,主要从事航空遥感、地理信息系统应用建模方面的研究。E-mail:lishanshan198352@163.com
  • 基金资助:

    国家自然科学基金项目(41130744、41171335);水利部公益性行业科研(200901091)和北京市自然科学基金项目(8101002)。

A Wavelet Auto-focusing Method of the Aerial Camera Based on CCD Mosaicing

LI Shanshan1,2,3,4, GONG Huili1,2,3,4, LIU Yang5*, HU Zhuowei1,2,3,4   

  1. 1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
    2. Key Laboratory of 3D Information and Acquisition and Application, MOE, Beijing 100048, China;
    3. Beijing Municipal Laboratory of Resources Environment and GIS, Beijing 100048, China;
    4. Key Laboratory of Integrated Disaster Assessment and Risk Governance, MCA, Beijing 100048, China;
    5. School of Astronautic, Harbin Institute of Technology, Harbin 150001, China
  • Received:2011-09-12 Revised:2012-02-02 Online:2012-02-25 Published:2012-02-24

摘要: 航空相机在执行对地观测任务时, 需要精确地调焦控制系统实时调整镜头焦距以获得清晰的图像。本文以航空成像运行模式下的CCD拼接结构的特点,提出了一种智能的航空相机小波调焦方法。将图像处理的清晰度评价方法引入航空相机调焦系统,并考虑人类视觉系统(HVS)的特点设置权值,使评价效果更符合人类的主观感受。本文分析了小波基函数的性质,通过比较得出了采用symlet2小波进行三层分解构造的清晰度评价函数,对不同细节丰富程度的图像具有适应性,得到了灵敏度鲁棒性好的评价函数。并选取细节丰富的区域作为评价函数,详细地介绍了结合航空成像设备CCD拼接结构的特点,进行智能调焦的实现原理及方法。理论分析及仿真试验证实了该算法的可行性及有效性。相比传统评价方法具有较高的灵敏度,小波调焦方法的效率能够满足航空相机实时调焦的需求。

关键词: 智能调焦, 小波分析, CCD拼接, 评价函数

Abstract: A wavelet auto-focusing method is proposed to consider the characteristic of the aerial camera CCD mosaicing structure. In order to evaluate the clarity of the image obtained by the aerial camera, an image assessing function is constructed based on the wavelet transform. The evaluation function is constructed by the wavelet coefficients of the image, and the characteristic of the CCD mosaicing structure was taken use to solve the problem that the evaluating values can't be compared for the variable images. On the basis of the wavelet evaluating function, the quality of the image was assessed. Then the CCD mosaicing structure was utilized to perform auto-focusing process, and a simulation was made to validate the novel method in the end. The weights are set according to the characteristic of the human vision system (HVS). In order to make the function adaptive to different images, the properties of the wavelet basis are analyzed. At the same time, the area with high frequency component is chosen as the evaluating area so as to improve the sensitivity of the evaluating function. And the flow chart of the focusing based on this method is described particularly. There are some innovation in this paper, such as: the weights of the wavelet coefficients in the evaluating function were set according to the characteristic of the wavelet transform and HVS, which is close to human subjective feel and insensitive to noise|the properties of the wavelet basis were analyzed so as to make the function adaptive to images with different high-frequency components|the wavelet basis with the best effect is symlet2 and the level of decomposing is three|the problem that auto-focusing of the aerial camera can't use digital images processing directly was solved by making use of the CCD mosaicing structure. All in all, this paper introduces a wavelet auto-focusing method based on digital image processing to aerial camera used the characteristic of CCD mosaicing structure. The function is close to the HVS subjective feeling and adaptive to objects with different frequency components, and this method is more sensitive and adaptive than traditional wavelet methods. It could obtain high image quality, and is suitable to the application in focusing system of the aerial camera.

Key words: autofocusing, evaluation function, wavelet analysis, CCD mosaicing