结合参数估计的天文图像极大似然恢复

耿则勋; 魏小峰; 沈忱

doi:10.3788/OPE.20132111.2943

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结合参数估计的天文图像极大似然恢复

信息科学 | 更新时间：2020-08-12

- 结合参数估计的天文图像极大似然恢复
- Astronomical image restoration basedMaximum-likelihood incorporated parameter estimation
- 光学精密工程 2013年21卷第11期页码：2943-2950
- 作者机构：
  
  解放军信息工程大学地理空间信息学院,河南郑州,450052
- 作者简介：
- 基金信息：
  
  国家新技术研究发展863 计划支持项目()
- DOI：10.3788/OPE.20132111.2943
  中图分类号： TP751.1
- 收稿日期：2013-05-02，
  
  修回日期：2013-06-13，
  
  网络出版日期：2013-11-22，
  
  纸质出版日期：2013-11-15
- 稿件说明：
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耿则勋, 魏小峰, 沈忱. 结合参数估计的天文图像极大似然恢复[J]. 光学精密工程, 2013,21(11): 2943-2950

GENG Ze-Xun, WEI Xiao-Feng, CHEN Chen. Astronomical image restoration basedMaximum-likelihood incorporated parameter estimation[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2943-2950
耿则勋, 魏小峰, 沈忱. 结合参数估计的天文图像极大似然恢复[J]. 光学精密工程, 2013,21(11): 2943-2950 DOI： 10.3788/OPE.20132111.2943.

GENG Ze-Xun, WEI Xiao-Feng, CHEN Chen. Astronomical image restoration basedMaximum-likelihood incorporated parameter estimation[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2943-2950 DOI： 10.3788/OPE.20132111.2943.

摘要

分析了Benvenuto等针对天文图像恢复提出的基于极大似然(ML)代价函数的有效逼近模型，由此提出了一种比传统ML收敛更快的图像恢复算法。该算法在未知点扩散函数（PSF）条件下，通过观测模糊图像，自适应估计湍流PSF，使PSF估计更符合成像环境；然后，将该算法与混合高斯泊松噪声的ML算法相结合，形成增强ML迭代算法。在迭代过程中动态更新PSF，交替执行恢复图像、去除噪声等策略。结果显示：对于点源目标图像，本文算法恢复图像的质量在峰值信噪比、均方误差以及平均对比度3个指标上分别提高了96.64%，69.26%和25.6%；对于真实湍流退化图像，恢复质量也有一定改善。结论表明：该方法可以使迭代过程收敛更稳定，图像恢复质量得到明显提高，非常适用于天文观测图像的高清晰恢复与重建。

Abstract

The effective approximation mode based on Maximum-likelihood (ML) function proposed by Benvenuto was analyzed for astronomy image restoration

then a new image restoration algorithm with convergence faster than that of traditional ML method was proposed. In this algorithm

PSF known a priori was not required. The turbulence PSF was estimated from observed blur images to make the PSF estimation more accordance with an imaging environment. By incorporating adaptive estimation of PSF into ML restoration

an enhanced ML algorithm was presented. Additionally

the PSF was updated successively during iteration

and the ML restoration and denoising were performed alternatively in iteration. The results show that the proposed algorithm works much better than ML does. Taking the point source image for an instance

proposed method improves the image quality by 96.64%，69.26% and 25.6% respectively on the peak signal to noise ratio

mean square error and the correlation coefficient. In conclusion

the algorithm allow the iterative process in ML algorithm to converge stably and the image quality to be improved. Experiment results show that the presented method can be used routinely in astronomical image restoration.

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references

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