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

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

张金刚^{1, 2, 3,},
相里斌^1, ,,
汶德胜³,
王书振^4,

1.
中国科学院光电研究院, 北京 100194
2.
中国科学院大学, 北京 100049
3.
中国科学院西安光学精密机械研究所, 陕西西安 710119
4.
西安电子科技大学计算机学院, 陕西西安 710071

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
计量
- 文章访问数: 2499
- HTML全文浏览量: 847
- PDF下载量: 230
- 被引次数: 0
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

Aberration correction technology based on chromatic aberration prior constraints

ZHANG Jin-gang^{1, 2, 3
,},
XIANG LI-bin^{1
, ,},
WEN De-sheng³,
WANG Shu-zhen^4
,

1.
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100194, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China
4.
School of Computer Science and Technology, Xidian University, Xi'an 710071, China

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

Corresponding author: XIANG LI-bin, E-mail:xiangli@aoe.ac.cn

摘要

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。
- 像差校正 /
- 解卷积 /
- 色差先验 /
- 交替方向乘子法
Abstract: A priori constraint of the chromatic aberration of "the edges of the same object should be in the same position in the three color channels" is proposed by analyzing the correlation between the three channels of the natural image edge in this paper.The priori constraint is mathematically approximated as the relative derivative of each channel.Based on this chromatic aberration prior constraint, a new aberration correction model, namely the image deconvolution model, is established, and a model solving algorithm based on ADMM is given.The experimental results show that this aberration correction technique can improve the peak SNR of image by more than 10 dB, which is much better than the current mainstream algorithms such as BM3D and YUV.Moreover, the visual image performance is greatly enhanced, thus basically meets the common optical system correction requirements for aberrations.
- aberration correction /
- deconvolution /
- chromatic aberration prior /
- alternating direction method of multipliers

HTML全文

图 1 光学系统成像原理示意图

Figure 1. Schematic diagram of optical system imaging principle

下载: 全尺寸图片幻灯片

图 2 色差先验示意图

Figure 2. Schematic diagram of chromatic aberration prior

下载: 全尺寸图片幻灯片

图 3 模糊核

Figure 3. Blur kernel

下载: 全尺寸图片幻灯片

图 4 清晰图片

Figure 4. Sharp image

下载: 全尺寸图片幻灯片

图 5 仿真得到的模糊图片

Figure 5. Blurred image

下载: 全尺寸图片幻灯片

图 6 采用本文算法获得的校正图像

Figure 6. Deblurred image by our proposed algorithm

下载: 全尺寸图片幻灯片

图 7 采用BM3D算法获得的校正图像

Figure 7. Deblurred image by BM3D algorithm

下载: 全尺寸图片幻灯片

图 8 采用YUV算法获得的校正图像

Figure 8. Deblurred image by YUV algorithm

下载: 全尺寸图片幻灯片

参考文献(20)

[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.