Dual dictionary sparse restoration of blurred images

FENG Liang; WANG Ping; XU Ting-fa; SHI Ming-zhu; ZHAO Feng

doi:10.3788/OPE.20111908.1982

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Dual dictionary sparse restoration of blurred images

Article | 更新时间：2020-08-12

- Dual dictionary sparse restoration of blurred images
- Optics and Precision Engineering Vol. 19, Issue 8, Pages: 1982-1989(2011)
- 作者机构：
  
  1. 北京理工大学光电学院,北京 100081
  2. 北京理工大学光电成像技术与系统教育部重点实验室,北京 100081
  3. 桂林电子科技大学,广西桂林 541004
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111908.1982
  CLC： TP391.4
- Received：09 June 2011，
  
  Revised：01 July 2011，
  
  Published Online：25 August 2011，
  
  Published：25 August 2011
- 稿件说明：
移动端阅览
冯亮, 王平, 许廷发, 石明珠, 赵峰. 运动模糊退化图像的双字典稀疏复原[J]. 光学精密工程, 2011,19(8): 1982-1989

FENG Liang, WANG Ping, XU Ting-fa, SHI Ming-zhu, ZHAO Feng. Dual dictionary sparse restoration of blurred images[J]. Editorial Office of Optics and Precision Engineering, 2011,19(8): 1982-1989
冯亮, 王平, 许廷发, 石明珠, 赵峰. 运动模糊退化图像的双字典稀疏复原[J]. 光学精密工程, 2011,19(8): 1982-1989 DOI： 10.3788/OPE.20111908.1982.

FENG Liang, WANG Ping, XU Ting-fa, SHI Ming-zhu, ZHAO Feng. Dual dictionary sparse restoration of blurred images[J]. Editorial Office of Optics and Precision Engineering, 2011,19(8): 1982-1989 DOI： 10.3788/OPE.20111908.1982.

摘要

为了消除图像中的运动模糊

提出了一种稀疏理论框架下的双字典稀疏复原方法

并分析了冗余字典的选取和迭代算法的实现。首先

建立了稀疏变换下的退化和复原模型

用Haar系数冗余字典将图像稀疏化

并用PCD阈值迭代算法对模糊图像进行收敛

得到复原图像。由于在有效去除复原图像的模糊的同时噪声在迭代过程中被放大并叠加在图像上

故从清晰图像库中训练了一个冗余字典进行第二次稀疏收敛来去除去模糊中被加权的噪声。实验结果表明

本文的方法对模糊退化图像有很好的复原效果

不仅有效地去除了运动模糊和噪声

并能在一定程度上保留边缘细节。最后拓展了两层稀疏优化模型

为以后在稀疏框架下的图像复原提供了新的思路。

Abstract

An image restoration method based on a dual dictionary was presented under the framework of sparse theory

and the choice of overcomplete dictionaries and the implementation of iteration methods were analyzed. Firstly

the degradation and the restoration models in the sparse theory were established

then the dictionary constructed by Haar coefficients was used to sparse the blurred image and shrink the image with Parallel Coordinate Decent(PCD) iteration algorithm to obtain the elementary deblurred image

in which the blur was removed efficiently

but the noise was weighted and added. For removing the weighted noise

the secondary dictionary from an image database was trained to shrink the deblurred image and get the final result. The results shows that the proposed method can restore the motion-blurred image efficiently

remove motion blur and noise and reserve the edge detail in some extents. Finally the two-level sparse optimization model was expanded and a new idea for the image restoration was presented under the sparse framework.

关键词

Keywords

references

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