基于简单透镜计算成像的图像复原重建

doi:10.13229/j.cnki.jdxbgxb201703038

吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 965-972.doi: 10.13229/j.cnki.jdxbgxb201703038

基于简单透镜计算成像的图像复原重建

王新华^{1, 2, 3}, 郝建坤^{2, 3}, 黄玮^{2, 3}, 欧阳继红¹

1.吉林大学计算机科学与技术学院,长春 130012;
2.中国科学院长春光学精密机械与物理研究所,长春 130033;
3.中国科学院应用光学国家重点实验室,长春 130033

收稿日期:2015-01-24 出版日期:2017-05-20 发布日期:2017-05-20
通讯作者: 欧阳继红(1964-),女,教授,博士生导师.研究方向:空间推理与数据挖掘.E-mail:ouyj@jlu.edu.cn
作者简介:王新华(1984-),男,助理研究员,博士.研究方向:计算成像技术.E-mail:xinhuajlu@163.com
基金资助:
国家自然科学基金项目(61170092); 吉林省科技发展计划项目(20160209006GX)

Image restoration and reconstruction based on simple lenses computational imaging

WANG Xin-hua^{1, 2, 3}, HAO Jian-kun^{2, 3}, HUANG Wei^{2, 3}, OUYANG Ji-hong¹

1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
3.State Key Laboratory of Applied Optics, Chinese Academy of Sciences, Changchun 130033, China

Received:2015-01-24 Online:2017-05-20 Published:2017-05-20

摘要/Abstract

摘要： 为了在精简光学镜头结构的情况下获取高像质图像,提出了基于简单透镜计算成像的图像复原重建方法。首先,通过分析光学系统点扩散函数空间变化特性,提出了基于空间变化的点扩散函数获取及测量方法。然后,采用基于自适应稀疏先验信息的空间变化点扩散函数图像分块复原算法,对简单透镜模糊成像进行复原重建。为了验证本文算法的有效性,采用数字仿真模拟和实际拍摄两种方式获取简单透镜成像结果。结果表明:本文算法能够有效地抑制图像边缘的振铃效应并较好地保留图像的细节。

关键词: 计算机应用, 信息处理技术, 计算成像, 空间变化点扩散函数, 分块复原法

Abstract: In order to obtain high quality image information under the condition of compact optical lens structure, an approach for image restoration and reconstruction based on simple lenses computational imaging was proposed. First, by analyzing the characteristics of the Space Variant Point Spread Function (SVPSF) of the optical system, the method of SVPSF acquisition and measurement was presented. Then, the image sectioned restoration algorithm with SVPSF based adaptive sparse prior information was developed, which was used successfully in the restoration of blurry images through simple lenses. To validate the algorithm, the imaging results were obtained from through simple lenses imaging by the digital simulation and real images. Experimental results indicate that the algorithm can efficiently suppress the ringing effect and preserve the details of the images.

Key words: computer application, information processing technology, computational imaging, space-variant point spread function (SVPSF), sectioned restoration method

中图分类号:

TP391.4

王新华, 郝建坤, 黄玮, 欧阳继红. 基于简单透镜计算成像的图像复原重建[J]. 吉林大学学报(工学版), 2017, 47(3): 965-972.

WANG Xin-hua, HAO Jian-kun, HUANG Wei, OUYANG Ji-hong. Image restoration and reconstruction based on simple lenses computational imaging[J]. 吉林大学学报(工学版), 2017, 47(3): 965-972.

参考文献

[1] 张宝龙,李丹,张少敬,等. 非球面鱼眼镜头设计及畸变校正算法研究[J]. 光学学报,2014,34(12): 1222001.
Zhang Bao-long, Li Dan, Zhang Shao-jing, et al. Design of aspheric fisheye lens and study of distortion correction algorithms[J]. Acta Optica Sinica,2014,34(12): 1222001.
[2] 史浩东,姜会林,张新,等. 基于矢量像差的自由曲面光学系统像差特性研究[J]. 光学学报,2015,35(12):1208003.
Shi Hao-dong,Jiang Hui-lin,Zhang Xin, et al. Research on freeform optical system aberration characteristic based on vector aberration[J]. Acta Optica Sinica,2015,35(12):1208003.
[3] Xu Xiang-ru, Huang Wei, Xu Ming-fei. Orthogonal polynomials describing polarization aberration for rotationally symmetric optical systems[J]. Optics Express,2015,23(21):27911-27919.
[4] 孙崇尚,丁亚林,王德江. 基于计算成像的宽视场高分辨相机研究进展[J]. 激光与光电子学进展,2013,50(3):030006.
Sun Chong-shang,Ding Ya-lin,Wang De-jiang. Advances in studies of wide-field high-resolution camera based on computational imaging[J]. Laser & Optoelectronics Progress,2013,50(3):030006.
[5] 徐树奎,涂丹,李国辉,等. 计算摄影综述[J]. 计算机应用研究,2010,27(11):4032-4039.
Xu Shu-kui,Tu Dan, Li Guo-hui, et al. Review of computational photography[J]. Application Research of Computers,2010,27(11):4032-4039.
[6] Raskar R, Tumblin J, Mohan A, et al. Computational photography[J]. Proc of Eurographics:State of the Art Report(STAR),2006,1(1):1-20.
[7] Nayar S K. Computational cameras: approaches, benefits and limits[R]. New York:Department of Computer Science, Columbia University,2011.
[8] Heide F, Rouf M, Hullin M B, et al. High-quality computational imaging through simple lenses[J]. ACM Transactions on Graphics,2013,32(5):149.
[9] 冯华君,陶小平,赵巨峰,等. 空间变化PSF图像复原技术的研究现状与展望[J]. 光电工程,2009,36(1):1-7.
Feng Hua-jun, Tao Xiao-ping, Zhao Ju-feng, et al. Review and prospect of image restoration with space-variant point spread function[J]. Opto-Electronic Engineering,2009,36(1):1-7.
[10] 徐明飞,黄玮. 高数值孔径投影光刻波像差的自动平衡优化[J]. 光学精密工程,2015,23(8):2143-2148.
Xu Ming-fei, Huang Wei. Automatic balancing of wavefront aberrations in high-numerical aperture lithographic lenses[J]. Optics and Precision Engineering,2015,23(8):2143-2148.
[11] Li Wei-li,Liu Yu,Wang Bin,et al. A computational photography algorithm for quality enhancement of single lens imaging deblurring[J]. Optik-International Journal for Light and Electron Optics,2015,126(21):2788-2792.
[12] 陶小平,冯华君,雷华,等. 一种空间变化PSF图像分块复原的拼接方法[J]. 光学学报,2009,29(3):648-653.
Tao Xiao-ping, Feng Hua-jun, Lei Hua,et al. A splicing method of sectioned restoration algorithm for images with sapce-variant point spread function[J]. Acta Optica Sinica,2009,29(3):648-653.
[13] Yu Gao-hang, Xue Wei, Zhou Yi. A nonmonotone adaptive projected gradient method for primal-dual total variation image restoration[J]. Signal Processing,2014,103:242-249.
[14] 刘伟豪,梅林,蔡烜. 稀疏梯度先验模型的正则化图像复原[J]. 中国图像图形学报,2012,17(12):1485-1491.
Liu Wei-hao, Mei Lin, Cai Xuan. Regularized image restoration algorithm on sparse gradient prior model[J]. Journal of Image and Graphics,2012,17(12):1485-1491.
[15] Levin A,Fergus R,Durand F,et al. Image and depth from a conventional camera with a coded aperture[J].ACM Trans on Graphics,2007,26(3):70-79.
[16] 闫河,闫卫军,李唯唯. 基于Lucy-Richardson算法的图像复原[J]. 计算机工程,2010,36(15):204-205,210.
Yan He,Yan Wei-jun,Li Wei-wei. Image restoration based on Lucy-Richardson algorithm[J]. Computer Engineering,2010,36(15):204-205,210.
[16] 王新华, 黄玮, 欧阳继红. 多探测器拼接成像系统实时图像配准[J]. 中国光学, 2015, 8(2): 211-219.
Wang Xin-hua, Huang Wei, Ouyang Ji-hong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219.
[17] 温雯,郝志峰,邵壮丰. 迭代重加权最小二乘法支持向量机快速算法研究[J]. 计算机科学,2010,37(8):224-228.
Wen Wen, Hao Zhi-feng, Shao Zhuang-feng. Study on the fast training algorithm of iteratively re-weighted least squares support vector machine[J]. Computer Science,2010,37(8):224-228.
[16] 王新华, 王晓坤. 十亿像素瞬态成像系统实时图像拼接[J]. 中国光学, 2015, 8(5): 785-793.
Wang Xin-hua, Wang Xiao-kun. Real time image mosaic of the transient gigapixel imaging system[J]. Chinese Optics, 2015, 8(5): 785-793.

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基于简单透镜计算成像的图像复原重建

Image restoration and reconstruction based on simple lenses computational imaging

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