Adaptive blind image restoration based on NAS-RIF algorithm

HUANG De-tian; WU Zhi-yong

doi:10.3788/OPE.20122009.2078

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Adaptive blind image restoration based on NAS-RIF algorithm

Article | 更新时间：2020-08-12

- Adaptive blind image restoration based on NAS-RIF algorithm
- Optics and Precision Engineering Vol. 20, Issue 9, Pages: 2078-2086(2012)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100039
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122009.2078
  CLC： TP391.4
- Received：21 April 2012，
  
  Revised：13 May 2012，
  
  Published：10 September 2012
- 稿件说明：
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黄德天, 吴志勇. 基于非负支撑域受限递归逆滤波的自适应图像盲复原[J]. 光学精密工程, 2012,20(9): 2078-2086

HUANG De-tian, WU Zhi-yong. Adaptive blind image restoration based on NAS-RIF algorithm[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2078-2086
黄德天, 吴志勇. 基于非负支撑域受限递归逆滤波的自适应图像盲复原[J]. 光学精密工程, 2012,20(9): 2078-2086 DOI： 10.3788/OPE.20122009.2078.

HUANG De-tian, WU Zhi-yong. Adaptive blind image restoration based on NAS-RIF algorithm[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2078-2086 DOI： 10.3788/OPE.20122009.2078.

摘要

针对原始非负支撑域受限递归逆滤波(NAS-RIF)算法存在的缺点

提出了一种自适应的NAS-RIF图像盲复原算法。首先

在NAS-RIF算法的代价函数中加入正则化约束项和空域加权因子

通过自适应地调整正则化参数和空域加权因子来改善算法的抗噪性能

并确保复原的逼真和平滑。然后

在算法的每次迭代中

采用图像分割技术找到准确的目标支持域

并用背景的平均值取代非均匀背景。最后

利用

步重置共轭梯度法优化代价函数

加快了算法的收敛速度。在不同信噪比条件下对两种模糊图像进行了实验

结果显示

采用本文算法得到的信噪比增益(SNR)分别为6.315 3 dB和8.910 6 dB

表明该算法具有较好的噪声抑制和边缘细节恢复效果。对低信噪比的退化图像

本文算法也能得到更好的复原结果。

Abstract

To overcome disadvantages of the original Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) algorithm

an adaptive algorithm for the blind image restoration based on NAS-RIF algorithm was proposed. Firstly

regularization terms and space weights were added to the cost function of the original NAS-RIF algorithm. Through adaptively modulating the regularization parameters and space weights

not only the noise resistance ability could be improved

but the restored image could be smoothed. Then

image segmentation technique was employed in each iteration to find the precise object support region

meanwhile

the non-uniform background was replaced by the average background. Finally

the

-step-restart conjugate gradient routine was applied to optimization of the cost function

and then the convergence rate was enhanced. The experiments on degraded images derived from two kinds of blur operators were performed under different SNR (Signal Noise Ratio) conditions

and the SNRs by proposed algorithm are 6.315 3 dB and 8.910 6 dB

respectively. The experiment results demonstrate that the proposed algorithm has a positive improvement in both reducing noises and preserving edges. Particularly

the proposed algorithm can obtain a better restoration result under a low SNR condition.

关键词

Keywords

references

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