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采用色差先验约束的像差校正技术

张金刚 相里斌 汶德胜 王书振

张金刚, 相里斌, 汶德胜, 王书振. 采用色差先验约束的像差校正技术[J]. 中国光学, 2018, 11(4): 560-567. doi: 10.3788/CO.20181104.0560
引用本文: 张金刚, 相里斌, 汶德胜, 王书振. 采用色差先验约束的像差校正技术[J]. 中国光学, 2018, 11(4): 560-567. doi: 10.3788/CO.20181104.0560
ZHANG Jin-gang, XIANG LI-bin, WEN De-sheng, WANG Shu-zhen. Aberration correction technology based on chromatic aberration prior constraints[J]. Chinese Optics, 2018, 11(4): 560-567. doi: 10.3788/CO.20181104.0560
Citation: ZHANG Jin-gang, XIANG LI-bin, WEN De-sheng, WANG Shu-zhen. Aberration correction technology based on chromatic aberration prior constraints[J]. Chinese Optics, 2018, 11(4): 560-567. doi: 10.3788/CO.20181104.0560

采用色差先验约束的像差校正技术

doi: 10.3788/CO.20181104.0560
基金项目: 

国家自然科学基金项目 61775219

国家自然科学基金项目 61771369

国家自然科学基金项目 61640422

国家自然科学基金项目 61540028

中国科学院装备预研联合基金项目 6141A01011601

详细信息
    作者简介:

    张金刚(1982-), 男, 陕西榆林人, 副研究员, 主要从事计算光学成像技术方面的研究。E-mail:zhjg007@126.com

    相里斌(1967—), 男, 山西人, 博士, 研究员, 主要从事光学工程与空间技术领域方面的研究。E-mail:xiangli@aoe.ac.cn

    王书振(1978—), 男, 山东聊城人, 博士, 副教授, 主要从事图像处理方面的研究。E-mail:shuzhenwang@xidian.edu.cn

  • 中图分类号: TP394.1;TH691.9

Aberration correction technology based on chromatic aberration prior constraints

Funds: 

National Natural Science Foundation of China 61775219

National Natural Science Foundation of China 61771369

National Natural Science Foundation of China 61640422

National Natural Science Foundation of China 61540028

Joiny Fund for Equipment Pre-Research of the Chinese Academy of Sciences 6141A01011601

More Information
  • 摘要: 本文通过分析自然图像的边缘3个通道之间的关联性, 提出"同一物体的边缘在3个颜色通道应处于相同位置"的色差先验约束, 该约束在数学上近似为各通道的相对导数相等, 基于此色差先验约束, 建立了一种新的像差校正模型即图像解卷积模型, 并给出了基于交替方向乘子法的模型求解算法。实验结果表明:本文的像差校正技术可以提升图像的峰值信噪比10 dB以上, 明显优于目前主流的BM3D和YUV算法, 并且视觉提升效果明显, 基本满足普通光学系统对像差的校正要求。
  • 图  1  光学系统成像原理示意图

    Figure  1.  Schematic diagram of optical system imaging principle

    图  2  色差先验示意图

    Figure  2.  Schematic diagram of chromatic aberration prior

    图  3  模糊核

    Figure  3.  Blur kernel

    图  4  清晰图片

    Figure  4.  Sharp image

    图  5  仿真得到的模糊图片

    Figure  5.  Blurred image

    图  6  采用本文算法获得的校正图像

    Figure  6.  Deblurred image by our proposed algorithm

    图  7  采用BM3D算法获得的校正图像

    Figure  7.  Deblurred image by BM3D algorithm

    图  8  采用YUV算法获得的校正图像

    Figure  8.  Deblurred image by YUV algorithm

  • [1] 李思雯, 徐超, 刘广荣, 等.大气湍流模糊图像的高分辨力复原算法[J].红外与激光工程, 2013, 42(12):3486-3490. doi: 10.3969/j.issn.1007-2276.2013.12.058

    LI S W, XU CH, LIU G R, et al..High resolution restoration algorithm of atmospheric turbulence blurred image[J].Infrared and Laser Engineering, 2013, 42(12):3486-3490.(in Chinese) doi: 10.3969/j.issn.1007-2276.2013.12.058
    [2] TORRALBA A, OLIVA A.Statistics of natural image categories[J].Network:Computation in Neural Systems, 2003, 14:391-412. doi: 10.1088/0954-898X_14_3_302
    [3] LI D X, ZHAO Y, XU D.Algorithm of blur identification and image restoration based on parameter estimation[J].Infrared and Engineering, 2010, 39(1):166-172. http://en.cnki.com.cn/Article_en/CJFDTOTAL-HWYJ201001040.htm
    [4] LEVIN A, WEISS Y, DURAND F, et al. . Efficient marginal likelihood optimization in blind deconvolution[C]. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), IEEE, 2011: 2657-2664.
    [5] MACHIKHIN A, BATSHEV V, POZHAR V.Aberration analysis of AOTF-based spectral imaging systems[J].Journal of the Optical Society of America A, 2017, 34(7):1109-1113. doi: 10.1364/JOSAA.34.001109
    [6] ASHKANI J, MANUCHEHR S, KNUT1 M, et al..An accelerated version of alternating direction method of multipliers for TV minimization in EIT[J].Applied Mathematical Modelling, 2016, 40(21-22):8985-9000. doi: 10.1016/j.apm.2016.05.052
    [7] YIN Q, GUO P, LIU H, et al..Blind deconvolution for astronomical spectrum extraction from two-dimensional multifiber spectrum images[J].Optics Express, 2017, 25(5):5133-5145. doi: 10.1364/OE.25.005133
    [8] CARCAMO M, ROMAN P E, CASASSUS S, et al..Multi-GPU maximum entropy image synthesis for radio astronomy[J].Astrophysics and Computing, 2017, 22:16-27. https://www.researchgate.net/publication/236658125_Current_Trends_in_Radio_Image_Synthesis
    [9] CHEN H, WANG Q, WANG C, et al..Image decomposition-based blind image deconvolution model by employing sparse representation[J].IET Image Processing, 2016, 10(11):908-925. doi: 10.1049/iet-ipr.2015.0734
    [10] MA L, ZHANG R, QU Z, et al..Blind image deconvolution using sparse and redundant representation[J].Optik, 2014, 125(23):6942-6945. doi: 10.1016/j.ijleo.2014.08.067
    [11] DONATELLI M, HUCKLE T, MAZZA M, et al..Image deblurring by sparsity constraint on the Fourier coefficients[J].Numerical Algorithms, 2016, 72(2):341-361. doi: 10.1007/s11075-015-0047-x
    [12] MCLAUGHLIN M J, LIN E U, BLASCH E, et al. . Modified deconvolution using wavelet image fusion[C]. IEEE Applied Imagery Pattern Recognition Workshop, IEEE, 2015: 4.
    [13] LI W L, LIU Y, YIN X Q, et al..Computational imaging through chromatic aberration corrected simple lenses[J].Journal of Modern Optics, 2017, 64(20):1-10. http://www.cs.ubc.ca/labs/imager/tr/2013/SimpleLensImaging/
    [14] LI W, LIU Y, YIN X, 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. doi: 10.1016/j.ijleo.2015.07.030
    [15] 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. https://www.reddit.com/r/weirdglass/comments/5xaks5/highquality_computational_imaging_through_simple/
    [16] HUANG P Q, ZENG X, SUN Q, et al..Super-resolving blurry multiframe images through multiframe blind deblurring using ADMM[J].Multimedia Tools and Applications, 2017, 76(11):13563-13579. doi: 10.1007/s11042-016-3770-y
    [17] SIXOU B, TOMA A, DENIS L, et al..Iterative choice of the optimal regularization parameter in TV image deconvolution[J].Journal of Physics:Conference Series, 2017, 9(4):1171-1191. http://iopscience.iop.org/1742-6596/464/1/012005
    [18] TURNES C K, BALCAN D, ROMBERG J. Image deconvolution via superfast inversion of a class of two-level Toeplitz matrices[C]. IEEE International Conference on Image Processing, IEEE, 2013.
    [19] DABOV K, FOI A, KATKOVNIK V, et al..Image denoising by sparse 3D transform-domain collaborative filtering[J].IEEE Trans.Image Process, 2007, 16(8):2080-2095. doi: 10.1109/TIP.2007.901238
    [20] CHRISTIAN J S, MICHAEL H, STEFAN H, et al. . Non-stationary correction of optical aberrations[C]. 13th IEEE International Conference on Computer Vision, Piscataway, NJ, USA: IEEE, 2011: 659-666.
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出版历程
  • 收稿日期:  2018-01-11
  • 修回日期:  2018-03-13
  • 刊出日期:  2018-08-01

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