Non-convex sparsity regularization for wave back restoration of space object images

GUO Cong-zhou; SHI Wen-jun; QIN ZHi-yuan; GENG Ze-xun

doi:10.3788/OPE.20162404.0902

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Non-convex sparsity regularization for wave back restoration of space object images

更新时间：2020-08-13

- Non-convex sparsity regularization for wave back restoration of space object images
- Optics and Precision Engineering Vol. 24, Issue 4, Pages: 902-912(2016)
- 作者机构：
  
  1. 信息工程大学地理空间信息学院,河南郑州,450001
  2. 信息工程大学理学院,河南郑州,450001
  3. 郑州升达经贸管理学院,河南郑州,451191
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162404.0902
  CLC： TP391.4
- Received：06 November 2015，
  
  Revised：12 January 2016，
  
  Published：25 April 2016
- 稿件说明：
移动端阅览
郭从洲, 时文俊, 秦志远等. 空间目标图像的非凸稀疏正则化波后复原[J]. 光学精密工程, 2016,24(4): 902-912

GUO Cong-zhou, SHI Wen-jun, QIN ZHi-yuan etc. Non-convex sparsity regularization for wave back restoration of space object images[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 902-912
郭从洲, 时文俊, 秦志远等. 空间目标图像的非凸稀疏正则化波后复原[J]. 光学精密工程, 2016,24(4): 902-912 DOI： 10.3788/OPE.20162404.0902.

GUO Cong-zhou, SHI Wen-jun, QIN ZHi-yuan etc. Non-convex sparsity regularization for wave back restoration of space object images[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 902-912 DOI： 10.3788/OPE.20162404.0902.

摘要

现有的空间目标图像波后处理方法多直接套用自然光学图像的复原技术

效果并不理想。本文通过分析空间目标图像的近似稀疏性和灰度值服从超拉普拉斯分布的独有特点

提出了一个采用正则化方法的非凸稀疏正则化空间目标图像复原模型。在数值计算过程中

根据交替方向乘数法将复原模型分解为两个子问题

对凸优化子问题采用快速傅里叶变换求解

对非凸优化子问题采用固定点迭代方法求解。文中设计了非凸稀疏正则化空间目标图像波后复原的完整算法流程

并针对模拟图像和真实空间目标图像进行了对比验证。结果显示:相对于最近的流行算法

提出方法的最大峰值信噪比提高了2 dB

最大平均结构相似度提高了0.17

最大信息熵提高了3.85

图像清晰度提高了2.65。

Abstract

The wave back restoration of space object images is usually performed by restoration methods for nature optical images

however

the restoration effect is not ideal. This article proposes a restoration model of a space object image based on non-convex sparsity regularization according to the approximate sparsity of the space object image and the features that the gray value submits to Hyper-Laplace distribution in a regularization way. With the alternating direction multiplier method

the restoration model is split into two sub-problems in the numerical solving process:Fast Fourier transformation is used to solve the convex sub-problem

while the fixed-point iteration is used to solve the nonconvex sub-problem. Then

it gives a complete process for the proposed wave back restoration method of space object images

and do an experiment to test and verify the simulated images and the real space object images. Compared results show that proposed method improves the largest peak signal to noise ratio by 2 dB

the average structural similarity by 0.17 and the information entropy and the image definition by 3.85 and 2.65

respectively.

关键词

Keywords

references

RODDIER F. Adaptive Optics in Astronomy[M]. UK:Cambridge University Press, 1999.

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)

TIKHONOV A N, ARSENIN U Y. Solution of Ill-Posed Problems[M]. New York:John Wiley & Sons, 1977:9-21.

ZHENG H, HELLWICH O. Adaptive data-driven regularization for variational image restoration in the BV space[C]. Proceedings of VISAPP'07, Barcelona, Spain, 2007, 53-60.

CHEN X J, NG M K, ZHANG C. Non-Lipschitz lp-regularization and box constrained model for image restoration[J]. IEEE Transaction on Image Processing, 2012, 21(12):4709-4721.

STAMATIOS L, AURELIEN B, MICHAEL U. Hessian-based norm regularization for image restoration with biomedical applications[J]. IEEE Transactions on Image Processing, 2012, 21(3):983-996.

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)

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

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.

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)

初永玲, 李绍春, 王枚. 非凸全变分正则化模糊图像复原模型研究[J]. 计算机工程与应用, 2011, 47(35):171-173. CHU Y L, LI SH CH, WANG M. Study on non-convex total variation regularization model for restoration of motion blurred image[J]. Computer Engineering and Applications, 2011, 47(35):171-173. (in Chinese)

陈明举. 基于统计特性的非局部均值去噪算法[J]. 液晶与显示, 2014, 29(3):450-454. CHEN M J. Non-local means image denoising algorithm based on statistical property[J]. Chinese Journal of Liquid Crystals and Display, 2014, 29(3):450-454. (in Chinese)

ALMEIDA M, ALMEIDA L. Blind and semi-blind deblurring of natural images[J]. IEEE Trans. Image Processing, 2010, 19(1):36-52.

KRISHNAN D, TAY T, FERGUS R. Blind deconvolution using a normalized sparsity measure[C]. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition IEEE Computer Society, 2011:233-240.

KOTERA J, ŠROUBEK F, MILANFAR P. Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors[J]. Computer Analysis of Images and Patterns, 2013:59-66.

SHAO W Z, LI H B, ELAD M. Bi-l0-l2-Norm Regularization for Blind Motion Deblurring[J]. Eprint Arxiv, 2014.

KRISHNAN D, BRUNA J, FERGUS R. Blind Deconvolution with Re-weighted Sparsity Promotion[J]. Arxiv Preprint Arxiv, 2013.

王亚强, 陈波. 一种改进的各向异性扩散超声图像去噪算法[J]. 液晶与显示, 2015, 30(2):310-316. WANG Y Q, CHEN B. Improved anisotropic diffusion ultrasound image denoising algorithm[J]. Chinese Journal of Liquid Crystals and Display, 2015, 30(2):310-316. (in Chinese)

闫敬文, 彭鸿, 刘蕾, 等. 基于L0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)

ZHANG J J, WANG Q. An iterative conjugate gradient regularization method for image restoration[J]. Journal of Information and Computing Science, 2010, 5(1):55-62.

SHI Y, CHANG Q, XU J. Convergence of fixed point iteration for deblurring and denoising problem[J]. Applied Mathematics and Computation, 2007, 189(2):1178-1185.

FANG H, YAN L. Multiframe blind image deconvolution with split Bregman method[J]. Optik International Journal for Light and Electron Optics, 2014, 125(1):446-451.

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.

HE C, HU C, YANG X, et al.. An adaptive total generalized variation model with augmented lagrangian method for image denoising[J]. Mathematical Problems in Engineering, 2014, 842(2):805-808.

HONG M, LUO Z Q. On the linear convergence of the alternating direction method of multipliers[J]. Eprint Arxiv, 2012.

YANG J, ZHANG Y. Alternating direction algorithms for L1 problems in compressive sensing[J].SIAM Journal on Scientific Computing, 2011, 33(1):250-278.

GETREUER P, GETREUER P. Total variation deconvolution using split Bregman[J]. Image Processing on Line, 2012, 2:158-174.

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

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

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