Blind restoration of nature optical images based on non-convex high order total variation regularization

GUO Cong-zhou; QIN ZHi-yuan

doi:10.3788/OPE.20152312.3490

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Blind restoration of nature optical images based on non-convex high order total variation regularization

Information Sciences | 更新时间：2020-08-13

- Blind restoration of nature optical images based on non-convex high order total variation regularization
- Optics and Precision Engineering Vol. 23, Issue 12, Pages: 3490-3499(2015)
- 作者机构：
  
  1. 信息工程大学理学院,河南郑州,450001
  2. 信息工程大学地理空间信息学院,河南郑州,450001
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152312.3490
  CLC： TP391.4
- Revised：05 November 2015，
  
  Published：25 December 2015
- 稿件说明：
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郭从洲, 秦志远,. 非凸高阶全变差正则化自然光学图像盲复原[J]. 光学精密工程, 2015,23(12): 3490-3499

GUO Cong-zhou, QIN ZHi-yuan,. Blind restoration of nature optical images based on non-convex high order total variation regularization[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3490-3499
郭从洲, 秦志远,. 非凸高阶全变差正则化自然光学图像盲复原[J]. 光学精密工程, 2015,23(12): 3490-3499 DOI： 10.3788/OPE.20152312.3490.

GUO Cong-zhou, QIN ZHi-yuan,. Blind restoration of nature optical images based on non-convex high order total variation regularization[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3490-3499 DOI： 10.3788/OPE.20152312.3490.

摘要

受噪声和图像边缘结构信息的影响

传统的图像盲复原方法易出现“振铃”、“拖尾”、“阶梯”等现象。为解决上述问题

本文利用图像的后验信息、点扩散函数(PSF)的稀疏性以及

两类范数在约束中的不同作用

提出了一种更一般的非凸高阶全变差正则化自然光学图像盲复原模型。针对提出模型的非凸优化问题

在数值求解过程中对模型的范数结构进行改进

引入Split-Bregman权值迭代方法

提高了计算精度。对人工模拟退化图像和真实图像进行了实验测试。结果表明

提出的方法能够对多种退化类型的图像进行有效复原

复原后的图像边缘保持良好

细节和纹理的处理都优于最近文献提出的模型。客观评价结果显示

相比最近文献的模型

提出模型的峰值信噪比最大可以提高2.08 dB

信息熵值最大可以提高1.14个单位。

Abstract

Influence by noise and image edge structure information

traditional blind image restoration methods usually result in special phenomena of ringing

tail and ladder. To solve these problems

this paper proposes a more general blind restoration model of nature optical images based on non-convex high order total variation regularization by using the posteriori information of an image

the sparse property of a Point Spread Function(PSF) and different advantages of norm

and norm

in restriction. In the numerical solving process

the Split-Bregman iteration method was introduced by improving the norm of the model structure to improve the calculation accuracy and to solve the non-convex optimization. The experimental test between artificial simulation degradation images and real images was performed. Results show that the proposed method restores effectively variety types of degenerated images

and the restored images have well edges and their texture details are better than that of the models in recent literatures. The objective appraisal indicates that the peak signal-to-noise ratio has increased by 2.08 dB and the largest improvement of the information entropy reaches to 1.14 units as compared to the latest literature models.

关键词

Keywords

references

阮秋琦,阮宇智(译). 数字图像处理[M]. 北京:电子工业出版社,2012. RUAN Q Q, RUAN Y ZH, (Tran.). Digital Image Processing[M]. Beijing: Electronic Industry Press, 2012. (in Chinese)

AYERS G R, DAINTY J C. Iterative blind deconvolution method and its application[J]. Optics Letters, 1990, 13(7): 547-549.

YOU Y, KAREH M. A regularization approach to joint blur identification and image restoration [J]. IEEE Transactions on Image Processing, 1996, 5(3): 416-428.

CHAN T F, WONG C K. Total variation blind deconvolution [J]. IEEE Transactions on Image Processing, 1998, 7(3): 370-375.

FERGUS R, SINGH B, HERTZMANN A,et al.. Removing camera shake from a single photograph[J]. ACM Trans. On Graphics, 2006, 25(3): 787-794.

KRISHNAN D, TAY T, FERGUS R. Blind deconvolution using a normalized sparsity measure[J]. IEEE Computer Vision and Pattern Recognition, 2011, 233-240.

刘成云,常发亮. 基于稀疏表示和Weber定律的运动图像盲复原[J]. 光学精密工程,2015,23(2):600-608. LIU CH Y, CHANG F L. Blind moving image restoration based on sparse representation and Weber's law[J]. Opt. Precision Eng., 2015, 23(2): 600-608. (in Chinese)

闫敬文,彭鸿,刘蕾,等. 基于正则化模糊核估计的遥感图像复原[J]. 光学精密工程,2014,22(9):2572-2579. YAN J W, PENG H, LIU L, et al.. Remote sensing image restoration based on zero-norm regularized kernel estimation [J]. Opt. Precision Eng., 2014, 22(9): 2572-2579. (in Chinese)

唐述,龚卫国. 高阶混合正则化图像盲复原方法[J]. 光学精密工程, 2013, 21(1):151-157. TANG SH, GONG W G. High-order hybrid regularization method for image blind restoration[J]. Opt. Precision Eng., 2013, 21(1):151-157. (in Chinese)

WU C L, TAI X C. Augmented Lagrangian method, dual methods, and split-bregman iteration for ROF, vectorial TV, and high order models[J]. Siam Journal on Imaging Sciences, 2010, 3(3): 300-339.

王莎,陈跃庭,冯华君,等. 基于TWIST-TV约束的图像去模糊方法[J]. 红外与激光工程,2014,43(6):2000-2006. WANG SH, CHEN Y T, FENG H J, et al.. TwIST-TV regularization based image deblurring method[J]. Infrared and Laser Engineering, 2014, 43(6):2000-2006. (in Chinese)

LI W, LI Q, GONG W, et al.. Total variation blind deconvolution employing split Bregman iteration[J]. Journal of Visual Communication & Image Representation, 2012, 23(3):409-417.

SHAO W,Z, ELAD M. Simple, accurate, and robust nonparametric blind super-resolution[EB/OL]. (2015-03-11)[2015-05-21].http://arxiv.org/abs/1503.03187.

LIN H, MARQUINA A, OSHER S J. Blind deconvolution using TV regularization and Bregman iteration[J]. International Journal of Imaging Systems and Technology, 2005, 15(1): 74-83.

KOTERA J, SROUBEK F, MILANFAR P. Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors[J]. Lecture Notes in Computer Science, 2013, 8048: 59-66.

马少贤,江成顺. 基于四阶偏微分方程的盲图像恢复模型[J]. 中国图象图形学报, 2010, 15(1):26-30. MA SH X, JIANG CH SH. A new method for image blind restoration based on fourth-order PDE[J]. Journal of Image and Graphics, 2010, 15(1):26-30. (in Chinese)

刘琨,王国宇,姬婷婷. 一种四阶P-Laplace图像盲复原方法[J]. 中国海洋大学学报, 2014, 44(9):110-115. LIU K, WANG G Y, JI T T. A method for fourth-order P-Laplace blind image restoration[J]. Periodical of Ocean University of China, 2014, 44(9):110-115. (in Chinese)

李伟红,许尚文,龚卫国. 基于非凸高阶全变差模型的 Split-Bregman权值迭代图像盲复原方法,中国:CN104134196A[P]. 2014-11-05. LI W H, XU S W, GONG W G. The Split -Bregman weight iterative blind image restoration method based on non-convex higher order total variation model,Chinese:CN104134196A[P]. 2014-11-05. (in Chinese)

KIM S, LIM H. Fourth-order partial differential equations for effective image denoising[J]. Proceedings of the Seventh Mississippi State-UAB Conference on Differential Equations & Computational Simulations, 2009:107-121.

KRISHNAN D, FERGUS R. Fast image deconvolution using hyper-laplacian priors [C]. Proceedings of Neural Information Processing Systems, 2009:1033-1041.

AUBERT G, KORNPROBST P. Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations[M]. New York: Springer Publishing Company, 2010.

蒋伟,胡学钢. 一种基于偏微分方程的图像复原新模型[J]. 计算机工程与应用, 2008, 44(14): 187-189. JIANG W, HU X G. Image restoration new model based on PDE[J]. Computer Engineering and Applications, 2008, 44(14): 187-189. (in Chinese)

易丽娅,鲁晓磊,黄本雄. 图像复原的小波域稀疏模型方法[J]. 红外与激光工程, 2010, 39(8):766-771. YI L Y, LU X L, HUANG B X. Image restoration based on wavelet domain sparse model[J]. Infrared and Laser Engineering, 2010, 39(8):766-771. (in Chinese)

LIU X W, HUANG L H. Split Bregman iteration algorithm for total bounded variation regularization based image deblurring[J]. Journal of Visual Communication Image Representation, 2010, 372(2): 486-495.

YANG Y F, PANG ZH F, SHI B L, et al.. Split Bregman method for the modified lot model in image denoising[J]. Applied Mathematics and Computation, 2011, 217(12): 5392-5403.

余瑞艳. 求解极小化问题的Bregman迭代算法[J]. 应用泛函分析学报,2012,14(4): 365-368. YU R Y. Bregman iterative algorithm for solving minimization problems [J]. Acta Analysis Functionalis Applicata, 2012, 14(4): 365-368. (in Chinese)

LI W H, LI Q L, GONG W G, et al.. Total variation blind deconvolution employing split Bregman iteration[J]. Journal of Visual Communication Image Representation, 2012, 23(3): 409-417.

耿则勋,陈波,王振国,等.自适应光学图像复原理论与方法[M]. 北京:科学出版社, 2010. GENG Z X, CHEN B, WANG ZH G, et al.. The Theory and Method about Adaptive Optics Image Restoration [M]. Beijing: Science press, 2010. (in Chinese)

KRISHNAN D, TAY T, FERGUS R. Blind deconvolution using a normalized sparsity measure[C]. IEEE Conference on Computer Vision & Pattern Recognition, 2011, 42(7):233-240.

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