Image restoration using adaptive weight matrix

ZHU Qi-dan; SUN Lei; CAI Cheng-tao

doi:10.3788/OPE.20132106.1592

您当前的位置：

首页 >

文章列表页 >

Image restoration using adaptive weight matrix

更新时间：2020-08-12

- Image restoration using adaptive weight matrix
- Optics and Precision Engineering Vol. 21, Issue 6, Pages: 1592-1597(2013)
- 作者机构：
  
  哈尔滨工程大学自动化学院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132106.1592
  CLC： TP394.1
- Received：22 November 2012，
  
  Revised：02 February 2013，
  
  Published Online：20 June 2013，
  
  Published：15 June 2013
- 稿件说明：
移动端阅览
朱齐丹孙磊蔡成涛. 应用自适应权值矩阵的图像复原[J]. 光学精密工程, 2013,21(6): 1592-1597

ZHU Qi-dan SUN Lei CAI Cheng-tao. Image restoration using adaptive weight matrix[J]. Editorial Office of Optics and Precision Engineering, 2013,21(6): 1592-1597
朱齐丹孙磊蔡成涛. 应用自适应权值矩阵的图像复原[J]. 光学精密工程, 2013,21(6): 1592-1597 DOI： 10.3788/OPE.20132106.1592.

ZHU Qi-dan SUN Lei CAI Cheng-tao. Image restoration using adaptive weight matrix[J]. Editorial Office of Optics and Precision Engineering, 2013,21(6): 1592-1597 DOI： 10.3788/OPE.20132106.1592.

摘要

由于自然图像先验模型的概率密度函数的非高斯性（稀疏性的）会导致图像复原的最优化函数不再是凸的，传统意义上的最大后验估计已不能很好地求得最优估计解，因此本文提出利用自适应权值矩阵来解决这一问题。进行图像复原时，首先利用自然图像先验模型有效地抑制振铃效应，然后利用基于自适应权值矩阵的共轭梯度算法来解决由于稀疏先验模型导致的最优化函数非凸的问题。权值矩阵可根据上一次的迭代结果进行更新，并能够纠正图像在上一次迭代过程中局部区域导数估计的错误。实验结果显示，利用本文方法得到复原图像的峰值信噪比(PSNR)为36.131 6，优于其它算法。最后，用本文方法对全景图像进行复原，得到了很好的复原效果，证明了本文方法的实用性和有效性。

Abstract

Because natural image filter response probability model is often a non-Gaussian form(sparse)

it leads to the optimization problem of image restoration to be a non-convex and the optimal estimation solution can not be obtained by traditional maximum a posterior estimation. Therefore

this paper proposes a image restoration algorithm based on adaptive weight matrix to solve the problem. With image restoration

a prior model for the natural image is used to restrain the ring effect effectively and the conjugate gradient algorithm based on adaptive weight matrix is used to solve the problem of the non-convex optimization function due to the sparse prior. The weight matrix updates according to the last iteration result and is able to correct the error of the local image derivative estimation in the last iteration process. Experiments show that Peak Signal to Noise Ratio( PSNR)gotten by proposed algorithm is 36.131 6

better than that from other algorithms. Finally

the panoramic image is restored by proposed method and the good results are also obtained

which demonstrates that the algorithm proposed is practical and effective.

关键词

Keywords

references

FERGUS R,SINGH B,HERTZMANN A,et al.. Removing camera shake from a single photograph[J]. ACM Trans.Graph, 2006, 25(3):787-794.[2]KRISHNAN D,FERGUS R.Fast image deconvolution using hyper-Laplacian priors[C]. NIPS,2009:1-9.[3]HACOHEN Y,FATTAL R,LISCHINSKI D. Image upsampling via texture hallucination[C]. Proceedings of the IEEE International Conference on Computational Photography (ICCP), 2010:1-8.[4]CHO T S,ZITNICH C L,JOSHI N,et al.. Image Restoration by matching gradient distributions.[J].IEEE Tran. on Pattern Analysis and Machine Intelligence.2012,34(4):683-694.[5]温博,张启衡,张建林. 应用自解卷积和增量Wiener滤波实现迭代盲图像复原[J]. 光学精密工程,2011,19(12):3049-3055.WEN B, ZHANG Q H,ZHANG J L. Realization of iterative blind image restoration by self deconvolution and increment Wiener filter [J].Opt. Precision Eng., 2012,34(4):683-694. (in Chinese)[6]ROTH S,BLACK M J. Fields of experts[J]. International Journal of Computer Vision,2009, 82(2):205-229.[7]WEISS,FREEMAN W T. What makes a good model of natural images [C]. CVPR, 2007:1-8.[8]CHO T S,JOSHI N,ZITNICK C L,et al.. A content-aware image prior [C].CVPR, 2010:169-176.[9]SCHMIDT U,GAO Q,ROTH S. A generative perspective on MRFs in low-level vision [C]. CVPR,2010:1751-1758.[10]邹谋炎.反卷积与信号复原[M]. 北京: 国防工业出版社, 2001:35-39.ZOU M Y. Deconvolution and Signal Recovery [M].Beijing: Nation Defense Industry Press,2001:35-39.(in Chinese)[11]TAPPEN M F,LIU C,ADELSON E H,et al.. Learning Gaussian conditional random fields for low-level vision [C].CVPR, 2007:1-8.[12]MEER P. Emerging Topics in Computer Vision[M]. Prentice Hall,2004.[13]ROTH S,BLACK M J. Fields of experts:A frame of experts for learning image priors[C].CVPR,2005:860-867.[14]SCHMIDT U,SCHELTEN K,ROTH S. Bayesian Deblurring with Integrated Noise Estimation [C].CVPR,2011:2625-2632.[15]曾吉勇, 苏显渝. 双曲面折反射全景成像系统[J].光学学报,2003, 23(9):1138-1142.ZENG J Y, SU X Y. Hype rboloidal catadioptric omnidirectioal imaging system[J].Acta Optica Sinica, 2003,23(9):1138-1142. (in Chinese)

Views

581

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-regularization image restoration method for X-ray images of integrated circuit

Restoration of blurred infrared image by adaptive double edge method

End-to-end image dehazing under separated features and collaborative network

Magneto-optical imaging restoration and recognition technology for damaged characters

Combined exposure computational imaging system and image restoration method

Related Author

MA Ge

LIN Sen

LI Zhifu

ZHAO Zhijia

ZOU Tao

Xueying CAO

Lin WANG

Qinyan TAN

Related Institution

School of Mechanical and Electrical Engineering， Guangzhou University

Infrared Detection Technology Research & Development Center of CASC

Shanghai Academy of Spaceflight Technology

School of Physics and Optoelectronic Engineering， Xidian University

School of Electronic and Information Engineering， Lanzhou Jiaotong University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰