基于小波变换的RL湍流退化图像复原算法

徐晓睿; 戴明; 尹传历

doi:10.3788/YJYXS20173210.0822

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基于小波变换的RL湍流退化图像复原算法

图像处理 | 更新时间：2020-08-12

- 基于小波变换的RL湍流退化图像复原算法
- RL turbulence degraded image restoration algorithm based on wavelet transform
- 液晶与显示 2017年32卷第10期页码：822-827
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  [ "徐晓睿(1992-), 女, 吉林长春人, 硕士研究生。2015年于吉林大学获得学士学位, 主要从事图像处理方面的研究。E-mail:xuxiaorui92@sina.cn" ]
  [ "戴明(1965-), 男, 湖北潜江人, 研究员, 博士生导师, 主要从事对地实时观测系统信号获取、图像处理及控制工程等方面的研究。E-mail:daiming@ciomp.ac.cn" ]
- 基金信息：
  
  国家自然科学基金(61405191);National Natural Science Foundation of China(61405191)
- DOI：10.3788/YJYXS20173210.0822
  中图分类号： TP751
- 收稿日期：2017-04-26，
  
  录用日期：2017-6-14，
  
  纸质出版日期：2017-10-05
- 稿件说明：
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徐晓睿, 戴明, 尹传历. 基于小波变换的RL湍流退化图像复原算法[J]. 液晶与显示, 2017,32(10):822-827.

Xiao-rui XU, Ming DAI, Chuan-li YIN. RL turbulence degraded image restoration algorithm based on wavelet transform[J]. Chinese journal of liquid crystals and displays, 2017, 32(10): 822-827.
徐晓睿, 戴明, 尹传历. 基于小波变换的RL湍流退化图像复原算法[J]. 液晶与显示, 2017,32(10):822-827. DOI： 10.3788/YJYXS20173210.0822.

Xiao-rui XU, Ming DAI, Chuan-li YIN. RL turbulence degraded image restoration algorithm based on wavelet transform[J]. Chinese journal of liquid crystals and displays, 2017, 32(10): 822-827. DOI： 10.3788/YJYXS20173210.0822.

摘要

为了从湍流退化图像中准确有效地恢复出目标图像，提出一种基于小波变换的RL湍流退化图像复原算法。该算法首先对湍流退化图像进行小波分解，可得到不同分解尺度下，不同频带的子图像。根据不同方向的高频子段的小波系数，估计出各个高频子段噪声方差，进而求得适用于各频段的自适应阈值，以这些阈值为软阈值法的临界条件分别对各频段的小波系数进行收缩，最后用RL算法去迭代小波重构后的图像来实现湍流退化图像的复原。为了验证该方法的有效性，分别用这两种算法在不同噪声条件下，对同一幅退化图像进行了仿真实验。改进后的算法使得两幅图像的峰值信噪比分别提高5.894 3 dB和7.108 4 dB。结果表明，本文的算法相比RL算法在复原效果上有一定的提高。

Abstract

In order to recover the target image accurately and effectively from the turbulence degraded image

a RL turbulence degraded image restoration algorithm based on wavelet transform is proposed. Firstly

the turbulence degraded image is decomposed by wavelet

and sub-images of different frequency bands can be obtained at different decomposition scales. According to the wavelet coefficients of the high frequency sub-segments in different directions

the noise variance of each high frequency sub-segment is estimated

and then the adaptive thresholds suitable for each frequency band are obtained. The critical conditions of these thresholds are the wavelet. And then the RL algorithm is used to iterate the reconstructed image to reconstruct the turbulence image. In order to verify the effectiveness of the method

the simulation results of the same degraded image are simulated by the two algorithms under different noise conditions. The improved method makes the peak signal to noise ratio of the two images increase by 5.894 3 dB and 7.108 4 dB. The results show that the proposed algorithm has a certain improvement in the recovery effect compared with the RL algorithm.

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