Image blind deblurring based on super total variation regularization with self adaptive threshold

ZHOU Luo-yu; ZHANG Bao; YANG Yang

doi:10.3788/OPE.20122012.2759

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Image blind deblurring based on super total variation regularization with self adaptive threshold

Article | 更新时间：2020-08-13

- Image blind deblurring based on super total variation regularization with self adaptive threshold
- Optics and Precision Engineering Vol. 20, Issue 12, Pages: 2759-2767(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. 中国科学院大学北京,中国,100049
  3. 中国科学院长春光学精密机械与物理研究所中国科学院航空光学成像与测量重点实验室,吉林长春,130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122012.2759
  CLC： TP391.4
- Received：04 September 2012，
  
  Revised：15 October 2012，
  
  Published：10 December 2012
- 稿件说明：
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周箩鱼, 张葆, 杨扬. 自适应阈值的超变分正则化图像盲复原[J]. 光学精密工程, 2012,20(12): 2759-2767

ZHOU Luo-yu, ZHANG Bao, YANG Yang. Image blind deblurring based on super total variation regularization with self adaptive threshold[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2759-2767
周箩鱼, 张葆, 杨扬. 自适应阈值的超变分正则化图像盲复原[J]. 光学精密工程, 2012,20(12): 2759-2767 DOI： 10.3788/OPE.20122012.2759.

ZHOU Luo-yu, ZHANG Bao, YANG Yang. Image blind deblurring based on super total variation regularization with self adaptive threshold[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2759-2767 DOI： 10.3788/OPE.20122012.2759.

摘要

针对一阶总变分盲复原块效应严重的问题

提出了一种自适应阈值的超变分正则化图像盲复原方法来恢复点扩散函数未知的退化图像。对总变分形式进行了分析

提出了超变分正则项

并给出了代价函数的数学模型。用估计的图像噪声确定模型中阈值的大小

然后引进3个辅助变量等价转化代价函数

以便简化后续计算并提高复原效果。最后

利用半二次规整化对模型迭代求解。实验结果表明

复原后图像细节增加且块效应减少

相对于目前已有的方法

信噪比提高了近1 dB。恢复效果表明该方法具有较大的实用价值。

Abstract

For the serious block effect in first-order variation image blind debluring

an image blind deblurring method based on super total variation with a self adaptive threshold was proposed to restore the images degraded by unknown Point Spread Function(PSF). Based on the analysis of the total variation model

the super total variation was proposed and the mathematical model of cost function was obtained. The threshold in the model was deduced by estimated image noises. Then

in order to simplify subsequent calculation and improve restoration effect

three auxiliary variables were introduced to transform the cost function into equivalent forms. Finally

semi-quadratic regularization was used to solve iteratively the cost function. The experimental results demonstrate that the restoration image has more details and fewer block effect. Compared with existing blind deblurring methods

the proposed algorithm can increase the Signal to Noise Ratio(SNR) of the restored image by 1dB. The restoration effect of the proposed method reveals its practicability in the blind image deblurring.

关键词

Keywords

references

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