Progress of degraded image restoration methods

Jun-shan LI; Ya-wei YANG; Jiao ZHANG; Jian-jun LI

doi:10.3788/YJYXS20183308.0676

您当前的位置：

首页 >

文章列表页 >

Progress of degraded image restoration methods

Image Processing | 更新时间：2020-07-06

- Progress of degraded image restoration methods
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 8, Pages: 676-689(2018)
- 作者机构：
  
  1.广东外语外贸大学南国商学院, 广东广州 510545
  2.中国人民解放军 96723部队, 广西柳州 545616
  3.西安卫星测控中心, 陕西西安 710043
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183308.0676
  CLC： TP391
- Received：27 February 2018，
  
  Accepted：28 May 2018，
  
  Published：05 August 2018
- 稿件说明：
移动端阅览
Jun-shan LI, Ya-wei YANG, Jiao ZHANG, et al. Progress of degraded image restoration methods[J]. Chinese journal of liquid crystals and displays, 2018, 33(8): 676-689.
DOI：

Jun-shan LI, Ya-wei YANG, Jiao ZHANG, et al. Progress of degraded image restoration methods[J]. Chinese journal of liquid crystals and displays, 2018, 33(8): 676-689. DOI： 10.3788/YJYXS20183308.0676.

摘要

飞行器和空间成像制导装备在大气中高速飞行时会受到湍流干扰，导致光学系统接收到的图像发生模糊降质、像素偏移、信噪比降低等问题，开展退化图像复原技术及方法研究就成为空间光学成像系统获得较高性能图像的重要途径。通过对退化图像复原技术研究进展的系统梳理和分析研究，本文首先介绍了图像退化模型，接着给出了退化图像复原方法的分类，然后比较系统地介绍了确定正则化图像复原方法、随机正则化图像复原方法、基于局部相似性的图像复原方法、基于示例学习的图像复原方法等几种新型的单幅退化图像复原方法，其后分析了视频复原的特征、介绍了新近的几种典型的视频图像复原方法，最后分析总结出了图像复原的难点所在。对于促进我国退化图像复原技术的研究和发展具有一定的参考价值。

Abstract

Owing to the existence of atmosphere

the transmission of the light waves will be interfered by the atmospheric turbulence. It will make the image blurred

the pixels got deviated and the image signal to noise ratio decreased. Therefore

the research of degraded image restoration technology has become an important way to obtain high performance image in space optical imaging system. In this paper

the research progress of degraded image restoration technology is systematically combed and analyzed. Firstly

the image degradation model is given and the classification of traditional restoration method is summarized. Secondly

degraded image restoration methods are systematically surveyed

especially new restoration technology such as regularization methods

random regularization methods

methods based on local similarity and exemplary learning. Furthermore

the feature of video restoration is analyzed

and several new methods of video image restoration are presented. Finally

the difficulty of image restoration is summarized. This review has a certain reference value for promoting the research and development of degraded image restoration technology.

关键词

Keywords

references

FURHAD M H, TAHTALI M, LAMBERT A. Restoring atmospheric-turbulence-degraded images[J]. Applied Optics, 2016, 55(19):5082-5090.

徐晓睿, 戴明, 尹传历.基于小波变换的RL湍流退化图像复原算法[J].液晶与显示, 2017, 32(10):822-827.

XU X R, DAI M, YIN C L. RL turbulence degraded image restoration algorithm based on wavelet transform[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(10):822-827. (in Chinese)

YE P, DOERMANN D. No-reference image quality assessment using visual codebook[J]. IEEE Transactions on Image Processing, 2012, 21(7):3129-3138.

RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/4):259-268.

OSHER S, FEDKIW R. Level Set Methods and Dynamic Implicit Surfaces[M]. New York:Springer, 2003.

GOLDLUECKE B, CREMERS D. An approach to vectorial total variation based on geometric measure theory[C]// Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Francisco, CA, USA: IEEE, 2010: 327-333.

OLIVEIRA J P, BIOUCAS-DIA J M, FIGUEIREDO M A T. Adaptive total variation image deblurring:a majorization-minimization approach[J]. Signal Processing, 2009, 89(9):1683-1693.

BECK A, TEBOULLE M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems[J]. IEEE Transactions on Image Processing, 2009, 18(11):2419-2434.

CHANTAS G, GALATSANOS N P, MOLINA R, et al. Variational bayesian image restoration with a product of spatially weighted total variation image priors[J]. IEEE Transactions on Image Processing, 2010, 19(2):351-362.

CAMPISI P, EGIAZARIAN K. Blind image Deconvolution: Theory and Applications[M]. Boca Raton:CRC Press, 2016.

陈新兵, 杨世植, 王先华, 等.基于曲面拟合与广义总变分的卫星图像盲复原[J].仪器仪表学报, 2009, 30(9):1896-1901.

CHEN X B, YANG S Z, WANG X H, et al. Satellite image blind restoration based on surface fitting and generalized total variation[J]. Chinese Journal of Scientific Instrument, 2009, 30(9):1896-1901. (in Chinese)

CAI J F, JI H, LIU C Q, et al . Blind motion deblurring from a single image using sparse approximation[C]// Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Miami, FL, USA: IEEE, 2009: 104-111.

BANHAM M R, KATSAGGELOS A K. Spatially adaptive wavelet-based multiscale image restoration[J]. IEEE Transactions on Image Processing, 1996, 5(4):619-634.

刘忠伟. 基于非局部约束和样例学习的图像复原[D]. 西安: 西安电子科技大学, 2012.

LIU Z W. Image restoration based non-local constrant and example learning[D]. Xi'an: Xidian University, 2012. (in Chinese)

LEVIN A, WEISS Y, DURAND F, et al . Understanding and evaluating blind deconvolution algorithms[C]// Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Miami, FL, USA: IEEE, 2009: 1964-1971.

王艮化, 练秋生.基于联合规整化约束的图像盲复原[J].燕山大学学报, 2012, 36(1):50-56.

WANGG H, LIAN Q S. Blind deconvolution based on fusion of regularization terms[J]. Journal of Yanshan University, 2012, 36(1):50-56. (in Chinese)

杨航, 吴笑天, 王宇庆.基于结构字典学习的图像复原方法[J].中国光学, 2017, 10(2):207-218.

YANG H, WU X T, WANG Y Q. Image restoration approach based on structure dictionary learning[J]. Chinese Optics, 2017, 10(2):207-218. (in Chinese)

JIA J Y. Single image motion deblurring using transparency[C]// Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition . Minneapolis, USA: IEEE, 2007: 1-8.

SHAN Q, JIA J Y, AGARWALA A. High-quality motion deblurring from a single image[J]. ACM Transactions on Graphics, 2008, 27(3):1-10.

常振春, 禹晶, 肖创柏, 等.基于稀疏表示和结构自相似性的单幅图像盲解卷积算法[J].自动化学报, 2017, 43(11):1908-1919.

CHANG Z C, YU J, XIAO C B, et al. Single image blind deconvolution using sparse representation and structural self-similarity[J]. Acta Automatica Sinica, 2017, 43(11):1908-1919. (in Chinese)

EFROS A A, LEUNG T K. Texture synthesis by non-parametric sampling[C]// Proceedings of the 7 th IEEE International Conference on Computer Vision . Kerkyra, Greece: IEEE, 1999: 1033-1038.

BUADES A, COIL B, MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling & Simulation, 2005, 4(2):490-530.

BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising[C]// Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego, USA: IEEE, 2005: 60-65.

TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]// Proceedings of the 6 th International Conference on Computer Vision . Bombay, India: IEEE, 1998: 839-846.

LI H J, SUEN C Y. A novel non-local means image denoising method based on grey theory[J]. Pattern Recognition, 2016, 49:237-248.

NGUYEN M P, CHUN S Y. Bounded self-weights estimation method for non-local means image denoising using minimax estimators[J]. IEEE Transactions on Image Processing, 2017, 26(4):1637-1649.

BUADES A, COLL B, MOREL J M. Nonlocal image and movie denoising[J]. International Journal of Computer Vision, 2008, 76(2):123-139.

曲光夫. 一种基于L 1 范数及高斯平滑的图像复原方法[D]. 兰州: 兰州大学, 2013.

QU G F. A new image restoration method by Gaussian smoothing with L 1 norm regularization[D]. Lanzhou: Lanzhou University, 2013. (in Chinese)

徐玉蕊, 刘乐, 王刚刚, 等.基于字典学习的大气湍流退化图像复原技术应用[J].吉林大学学报(信息科学版), 2016, 34(1):153-157.

XU Y R, LIU L, WANG G G, et al. Restoration of atmospheric turbulence degraded image based on dictionary learning[J]. Journal of Jilin University (Information Science Edition), 2016, 34(1):153-157. (in Chinese)

LIU H F, XIONG R Q, ZHANG X F, et al. Nonlocal gradient sparsity regularization for image restoration[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(9):1909-1921.

刘建伟, 崔立鹏, 罗雄麟.结构稀疏模型[J].计算机学报, 2017, 40(6):1309-1337.

LIU J W, CUI L P, LUO X L. Structured sparse models[J]. Chinese Journal of Computers, 2017, 40(6):1309-1337. (in Chinese)

YU G S, SAPIRO G, MALLAT S. Solving inverse problems with piecewise linear estimators:from Gaussian mixture models to structured sparsity[J]. IEEE Transactions on Image Processing, 2012, 21(5):2481-2499.

SANDEEP P, JACOB T. Single image super-resolution using a joint GMM method[J]. IEEE Transactions on Image Processing, 2016, 25(9):4233-4244.

ROTH S, BLACK M J. Fields of experts: a framework for learning image priors[C]// Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego, CA, USA: IEEE, 2005: 860-867.

TAKEDA H, FARSIU S, MILANFAR P. Deblurring using regularized locally adaptive kernel regression[J]. IEEE Transactions on Image Processing, 2008, 17(4):550-563.

LI Y J, ZHANG J W, WANG M N. Improved BM3D denoising method[J]. IET Image Processing, 2017, 11(12):1197-1204.

徐健, 常志国, 张小丹.采用交替K-奇异值分解字典训练的图像超分辨率算法[J].武汉大学学报·信息科学版, 2017, 42(8):1137-1143.

XU J, CHANG Z G, ZHANG X D. Image super-resolution based on alternate K-singular value decomposition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8):1137-1143. (in Chinese)

卢振波. 基于正则化优化的图像视频复原方法研究[D]. 合肥: 中国科学技术大学, 2017. http://cdmd.cnki.com.cn/Article/CDMD-10358-1017065337.htm

LU Z B. Researches on image and video restoration based on regularization optimization[D]. Hefei: University of Science and Technology of China, 2017. (in Chinese)

CHRISTOU J C, ROORDA A, WILLIAMS D R. Deconvolution of adaptive optics retinal images[J]. Journal of the Optical Society of America A, 2004, 21(8):1393-1401.

张姣, 李俊山, 李成, 等.基于低秩稀疏分解的湍流退化图像序列的盲去卷积算法[J].光电子·激光, 2015, 26(7):1374-1380.

ZHANG J, LI J S, LI C, et al. A multi-frame blind deconvolution algorithm based on low-rank decomposition for turbulence-degraded images[J]. Journal of Optoelectronics·Laser, 2015, 26(7):1374-1380. (in Chinese)

SCHULZ T J. Multiframe blind deconvolution of astronomical images[J]. Journal of the Optical Society of America A, 1993, 10(5):1064-1073.

洪汉玉, 张天序, 余国亮.基于Poisson模型的湍流退化图像多帧迭代复原算法[J].宇航学报, 2004, 25(6):649-654.

HONG H Y, ZHANG T Y, YU G L. Interative multi-frame restoration algorithm of turbulence-degraded images based on poisson model[J]. Journal of Astronautics, 2004, 25(6):649-654. (in Chinese)

杨秋英, 赵剡.相关序列图像复原算法研究[J].系统工程与电子技术, 2007, 29(9):1546-1550.

YANG Q Y, ZHAO Y. Restoration algorithm of sequence images in aero-optics[J]. Systems Engineering and Electronics, 2007, 29(9):1546-1550. (in Chinese)

杨秋英, 赵剡, 许东.基于功率谱AR模型估计的序列图像复原算法研究[J].红外与激光工程, 2006, 35(S1):147-151.

YANG Q Y, ZHAO Y, XU D. Restoration sequence images based on power spectrum AR modeling estimation[J]. Infrared and laser Engineering, 2006, 35(S1):147-151. (in Chinese)

WANG R X, TAO D C. Non-local auto-encoder with collaborative stabilization for image restoration[J]. IEEE Transactions on Image Processing, 2016, 25(5):2117-2129.

DAI S Y, WU Y. Motion from blur[C]// Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition . Anchorage, AK, USA: IEEE, 2008: 1-8.

李俊山, 杨亚威, 朱子江, 等.非凸性优化与动态自适应滤波的湍流退化视频复原[J].液晶与显示, 2017, 32(6):482-490.

LI J S, YANG Y W, ZHU Z J, et al. Turbulence-degraded video restoration based on non-convexity optimization and dynamical adaptive filtering[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(6):482-490(in Chinese)

KIMB. Numerical optimization methods for image restoration[D]. Stanford: Stanford University, 2002. https://www.researchgate.net/publication/36134223_Numerical_optimization_methods_for_image_restoration

GOLUB G H, VAN LOAN C F. Matrix Computation[M]. 2nd ed. Baltimore:Johns Hopkins University Press, 1989.

CHAN S H, NGUYEN T Q. LCD motion blur:modeling, analysis, and algorithm[J]. IEEE Transactions on Image Processing, 2011, 20(8):2352-2365.

NG M K, SHEN H F, LAM E Y, et al. A total variation regularization based super-resolution reconstruction algorithm for digital video[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2007:074585.

BELEKOS S P, GALATSANOS N P, KATSAGGELOS A K. Maximum a posteriori video super-resolution using a new multichannel image prior[J]. IEEE Transactions on Image Processing, 2010, 19(6):1451-1464.

CHOI M G, GALATSANOS N P, KATSAGGELOS A K. Multichannel regularized iterative restoration of motion compensated image sequences[J]. Journal of Visual Communication and Image Representation, 1996, 7(3):244-258.

MANNOS J, SAKRISON D. The effects of a visual fidelity criterion of the encoding of images[J]. IEEE Transactions on Information Theory, 1974, 20(4):525-536.

NILLN. A visual model weighted cosine transform for image compression and quality assessment[J]. IEEE Transactions on Communications, 1985, 33(6):551-557.

HONTSCHI, KARAM L J. Adaptive image coding with perceptual distortion control[J]. IEEE Transactions on Image Processing, 2002, 11(3):213-222.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Turbulence-degraded video restoration based on non-convexity optimization and dynamical adaptive filtering

Related Author

Jun-shan LI

Ya-wei YANG

Zi-jiang ZHU

Jiao ZHANG

Yao-hua DENG

Related Institution

The Institute of Information Science & Technology, Guangdong University of Foreign Studies South China Business College

Department of Information Engineering, Rocket Force University of Engineering

Unit of People's Liberation Army

School of Electro-Mechanical Engineering, Guangdong University of Technology

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@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