基于P系统的湍流模糊图像盲复原

高志升; 沈沉; 李谣顺; 杨淼

doi:10.3788/OPE.20172513.0304

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基于P系统的湍流模糊图像盲复原

信息科学 | 更新时间：2020-08-13

- 基于P系统的湍流模糊图像盲复原
- Blind restoration of atmospheric turbulence images based on P system
- 光学精密工程 2017年25卷第10s期页码：304-311
- 作者机构：
  
  西华大学计算机与软件工程学院,四川成都,610039
- 作者简介：
- 基金信息：
  
  教育部春晖计划资助项目（No.Z2016149）()
- DOI：10.3788/OPE.20172513.0304
  中图分类号： TP394.41
- 收稿日期：2017-07-10，
  
  修回日期：2017-07-25，
  
  纸质出版日期：2017-11-25
- 稿件说明：
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高志升, 沈沉, 李谣顺等. 基于P系统的湍流模糊图像盲复原[J]. 光学精密工程, 2017,25(10s): 304-311

GAO Zhi-sheng, SHEN Chen, LI Yao-shun etc. Blind restoration of atmospheric turbulence images based on P system[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 304-311
高志升, 沈沉, 李谣顺等. 基于P系统的湍流模糊图像盲复原[J]. 光学精密工程, 2017,25(10s): 304-311 DOI： 10.3788/OPE.20172513.0304.

GAO Zhi-sheng, SHEN Chen, LI Yao-shun etc. Blind restoration of atmospheric turbulence images based on P system[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 304-311 DOI： 10.3788/OPE.20172513.0304.

摘要

地基望远镜的成像过程会受到复杂多变的因素干扰，严重影响了对空间目标的高精度观测。本文运用长时曝光情况下的大气湍流传递函数模型，提出了基于P系统估计湍流点扩散函数的空间目标图像复原算法。采用非参考单帧图像评估指标作为优化目标函数，并运用P系统优化方法快速求取大气相干长度和功率谱密度比，结合维纳反卷积算法重建图像。通过实验与五种主流盲复原算法进行比较，算法具有最好的湍流模糊图像复原效果，其中在仿真图像上本文方法平均梯度、边缘强度评价分别为3.74和39.92，真实图像实验本文方法信息熵、边缘强度分别为5.66和61.61，总体上算法平均评价高于对比方法10%以上。

Abstract

The image process of ground-based telescopes is affected by complicated and variable factors

which seriously influences high-precision observation of space targets. Herein

a spatial object image restoration algorithm based on the P system was proposed based on the atmospheric turbulence transfer function model under long-term exposure. The non-reference single-frame image evaluation index was taken as the optimization objective function

and the P system optimization method was used to quickly obtain the atmospheric coherence length and spectral density ratio

thus reconstructing the image with the Wiener deconvolution algorithm. The algorithm was compared with five main blind restoration algorithms experimentally

which shows the proposed algorithm has the best turbulence deblurring image restoration effect. In terms of the simulated images

the average gradient and edge strength index of the proposed method are 3.74 and 39.92 respectively. In terms of real images

the entropy and edge strength of the method are 5.66 and 61.61 respectively. Generally

the average evaluation of the algorithm is more than 10% above the contrast method.

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