摘要
大气湍流扰动严重影响了空间目标的观测、成像和识别。在目标图像的重建过程中,对成像系统和大气条件先验知识的缺乏使PSF(Point Spread Function)的估计成为解决病态问题的重要内容。在NSWT(Non-Subsampled Wavelet Transform)和Fried参数的理论基础上,根据不同尺度下小波变换模极大值和高斯点扩散函数方差的关系,结合Fried参数提出一种新的估计方法,即基于非抽取小波变换模极大值的PSF估计算法。实验结果证明,该估计算法是确实可行的,重建的PSF能够有效地改善因受大气湍流影响的图像解卷积的效果。
The observed object images are severely blurred because of the influence of atmospheric turbulence. Lack of prior information in imaging system and atmospheric condition makes point spread function （PSF） estimation be an essential part of image restoration. Based on the NSWT and Fried parameter theory, this paper deduced a new PSF accurate estimation algorithm. Using the relation among the local maxima of the modulus of the non-subsampled wavelet at different scales, Lipschitz exponent and variance, the variance of a PSF was computed, which was used to restored the object image. The experimental results showed that the algorithm was capable of improving the quality of super-resolution image reconstruction, and was of good application value.
出处
《测绘科学技术学报》
北大核心
2008年第1期64-67,共4页
Journal of Geomatics Science and Technology
基金
国家自然科学基金资助项目(60778051)
国家863计划资助项目(2007AA703418A
2006AA12Z110)
关键词
PSF
NSWT
模极大值
Fried参数
point spread function
non-subsampled wavelet transform
maxima of the modulus
Fried parameter
