自适应阈值的超变分正则化图像盲复原

周箩鱼; 张葆; 杨扬

doi:10.3788/OPE.20122012.2759

您当前的位置：

首页 >

文章列表页 >

自适应阈值的超变分正则化图像盲复原

信息科学 | 更新时间：2020-08-13

- 自适应阈值的超变分正则化图像盲复原
- Image blind deblurring based on super total variation regularization with self adaptive threshold
- 光学精密工程 2012年20卷第12期页码：2759-2767
- 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：
  
  国家973重点基础研究发展计划资助项目(No.2009CB72400102A)()
- DOI：10.3788/OPE.20122012.2759
  中图分类号： TP391.4
- 收稿日期：2012-09-04，
  
  修回日期：2012-10-15，
  
  纸质出版日期：2012-12-10
- 稿件说明：
移动端阅览
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

XU Q S, SU J, LIU T T. A detection and recognition method for prohibition traffic signs. Proceedings of IEEE International Conference on Image Analysis and Signal Processing, Xiamen, P.R. China: ICIASP, 2010: 583-586.[2] WANG Y P, SHI M P, WU T. A method of fast and robust for traffic sign recognition. Proceedings of 5th International Conference on Image and Graphics, Xi'an, P.R. China: ICIG, 2009:891-895.[3] GU Y L, YENDO T, TEHRANI M P, et al.. A new vision system for traffic sign recognition. Proceedings of IEEE Intelligent Vehicles Symposium, San Diego, CA: IVS, 2010: 7-12.[4] 陈洪波,王强,徐晓蓉,等. 用改进的Hough变换检测交通标志图像的直线特征[J]. 光学精密工程,2009,17(5):1111-1118. CHEN H B, WANG Q, XU X R, et.al.. Line detection in traffic sign image based on improved Hough transforms [J]. Opt. Precision Eng., 2009,17(5):1111-1118. (in Chinese)[5] TANG J, LIANG X, CHENG F Y, et al.. A method for traffic signs detection, tracking and recognition. Proceedings of 5th International Conference on Computer Science and Education, Changsha, P. R. China: ICCSE, 2010:189-194.[6] JOSHI M, GINGH M J, DALELA S. Automatic colored traffic sign detection using optoelectronic correlation architectures. Proceedings of IEEE International Conference on Vehicular Electronics and Safety, Columbus, OH: ICVES, 2008: 75-78. [7] ANDREY V, JO K H. Automatic detection and recognition of traffic signs using geometric structure analysis. Proceedings of International Joint Conference on SICE-ICASE, Busan, Korea:ICSCIE-ICASE, 2006:1451-1456.[8] RUGHOOPUTH S D D V, BUOOTUN H, RUGHOOPUTH H C S. Pulse coded neural networks for sign recognition for navigation. Proceedings of IEEE International Conference on Industrial Technology, Maribor, Slovenia:ICIT, 2003,1:89-94.[9] WANG ZH H, TIE Y, LIU Y P. Design and implementation of image fusion system. Proceedings of International Conference on Computer Application and System Modeling, Taiyuan, P.R. China: ICCASE, 2010,1:140-143.[10] 武治国,王延杰,李桂菊. 应用小波变换的自适应脉冲耦合神经网络在图像融合中的应用[J]. 光学精密工程,2010,18(3): 708-715. WU ZH G, WANG Y J, LI G J. Application of adaptive PCNN based on wavelet transform to image fusion [J]. Opt. Precision Eng., 2010, 18(3):708-715. (in Chinese)[11] WANG ZH B, MA Y D, CHENG F Y, et al.. Review of pulse-coupled neural networks [J]. Image and Vision Computing, 2010, 28 (10): 5-13.[12] 刘相滨,邹北骥,孙家广. 基于边界跟踪的快速欧氏距离变换算法[J]. 计算机学报,2006,29(2): 317-323. LIU X B, ZOU B J, SUN J G. Fast euclidean distance transform based on contour tracking [J]. Chinese Journal of Computers, 2006,29(2):317-323. (in Chinese)[13] 陆宗骐,朱煜. 用带形状校正的腐蚀膨胀实现Euclidean距离变换[J]. 中国图像图形学报,2010,15(2):294-300. LU Z Q, ZHU Y. Implementation of Euclidean distance transform using erosion and dilation with form correction [J]. Journal of Image and Graphics, 2010, 15(2):294-300. (in Chinese)

浏览量

483

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据