Multi-frame blind deconvolution of solar images via second-order total generalized variation

Wang Shuai; He Chunyuan; Rong Huiqin; Bao Hua; Hou Jialin; Rao Changhui

doi:10.12086/oee.2023.220207

Article navigation > Opto-Electronic Engineering > 2023 Vol. 50 > No. 2 > 220207

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Wang S, He C Y, Rong H Q, et al. Multi-frame blind deconvolution of solar images via second-order total generalized variation[J]. Opto-Electron Eng, 2023, 50(2): 220207. doi: 10.12086/oee.2023.220207

Citation:

Wang S, He C Y, Rong H Q, et al. Multi-frame blind deconvolution of solar images via second-order total generalized variation[J]. Opto-Electron Eng, 2023, 50(2): 220207. doi: 10.12086/oee.2023.220207

Multi-frame blind deconvolution of solar images via second-order total generalized variation

1.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324003, China
2.
University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
3.
Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
4.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
5.
66389 Troops, Zhengzhou, Henan 450000, China

Fund Project: National Natural Science Foundation of China (11727805, 11703029, 11733005), the Municipal Government of Quzhou (2022D020)

More Information

Corresponding author: hbao@ioe.ac.cn

Received Date 27 August 2022

Revised Date 02 December 2022

Accepted Date 03 January 2023

Published Date 25 February 2023

Abstract

Abstract

Blind deconvolution is one of the commonly used post-reconstruction methods for adaptive optics images. In order to improve the reconstruction performance of blind deconvolution on solar (adaptive optics) images, a space-variant multi-frame blind deconvolution model based on second-order total generalized variation is proposed. It first solves the proposed space-invariant blind deconvolution model via second-order total generalized variation by the alternating minimization and half-quadratic splitting method. Then, according to the characteristics of wide field-of-view solar images which are anisoplanatic, the space-variant in the proposed algorithm is implemented by overlapping image segmentation and weighted stitching. Finally, the reconstruction experiment and analysis are carried out on the real solar images observed by the one-meter New Vacuum Solar Telescope (NVST). The results show that the proposed algorithm has good image reconstruction performance in both subjective visual effects and objective indexes.
- multi-frame blind deconvolution /
- second-order total generalized variation /
- solar images /
- adaptive optics

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References

[1]	鲍华, 饶长辉, 田雨, 等. 自适应光学图像事后重建技术研究进展[J]. 光电工程, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730 CrossRef Google Scholar Bao H, Rao C H, Tian Y, et al. Research progress on adaptive optical image post reconstruction[J]. Opto-Electron Eng, 2018, 45(3): 170730. doi: 10.12086/oee.2018.170730 CrossRef Google Scholar
[2]	Miura N, Baba N. Segmentation-based multiframe blind deconvolution of solar images[J]. J Opt Soc Am A, 1995, 12(9): 1858−1866. doi: 10.1364/JOSAA.12.001858 CrossRef Google Scholar
[3]	Löfdahl M G, Scharmer G B. Wavefront sensing and image restoration from focused and defocused solar images[J]. Astron Astrophys, 1994, 107: 243−264. Google Scholar
[4]	Seldin J H, Paxman R G. Phase-diverse speckle reconstruction of solar data[J]. Proc SPIE, 1994, 2302: 268−280. doi: 10.1117/12.188044 CrossRef Google Scholar
[5]	龙潇, 鲍华, 饶长辉, 等. 一种并行加速改进的快速相位解包裹算法[J]. 光电工程, 2020, 47(12): 200111. doi: 10.12086/oee.2020.200111 CrossRef Google Scholar Long X, Bao H, Rao C H, et al. Improved fast phase unwrapping algorithm based on parallel acceleration[J]. Opto-Electron Eng, 2020, 47(12): 200111. doi: 10.12086/oee.2020.200111 CrossRef Google Scholar
[6]	Von Der Lüehe O. Speckle imaging of solar small scale structure. I-methods[J]. Astron Astrophys, 1993, 268(1): 374−390. Google Scholar
[7]	Ramos A A, De La Cruz Rodríguez J, Yabar A P. Real-time, multiframe, blind deconvolution of solar images[J]. Astron Astrophys, 2018, 620: A73. doi: 10.1051/0004-6361/201833648 CrossRef Google Scholar
[8]	Wang S, Chen Q Q, He C Y, et al. Blind restoration of solar images via the Channel Sharing Spatio-temporal Network[J]. Astron Astrophys, 2021, 652: A50. doi: 10.1051/0004-6361/202140376 CrossRef Google Scholar
[9]	Guo Y M, Zhong L B, Min L, et al. Adaptive optics based on machine learning: a review[J]. Opto-Electron Adv, 2022, 5(7): 200082. doi: 10.29026/oea.2022.200082 CrossRef Google Scholar
[10]	Ayers G R, Dainty J C. Iterative blind deconvolution method and its applications[J]. Opt Lett, 1988, 13(7): 547−549. doi: 10.1364/OL.13.000547 CrossRef Google Scholar
[11]	Davey B L K, Lane R G, Bates R H T. Blind deconvolution of noisy complex-valued image[J]. Opt Commun, 1989, 69(5–6): 353−356. doi: 10.1016/0030-4018(89)90018-7 CrossRef Google Scholar
[12]	Fish D A, Brinicombe A M, Pike E R, et al. Blind deconvolution by means of the Richardson–Lucy algorithm[J]. J Opt Soc Am A, 1995, 12(1): 58−65. doi: 10.1364/JOSAA.12.000058 CrossRef Google Scholar
[13]	Kundur D, Hatzinakos D. A novel blind deconvolution scheme for image restoration using recursive filtering[J]. IEEE Trans Signal Process, 1998, 46(2): 375−390. doi: 10.1109/78.655423 CrossRef Google Scholar
[14]	Chan T F, Wong C K. Total variation blind deconvolution[J]. IEEE Trans Image Process, 1998, 7(3): 370−375. doi: 10.1109/83.661187 CrossRef Google Scholar
[15]	Schulz T J. Multiframe blind deconvolution of astronomical images[J]. J Opt Soc Am A, 1993, 10(5): 1064−1073. doi: 10.1364/JOSAA.10.001064 CrossRef Google Scholar
[16]	Harmeling S, Hirsch M, Sra S, et al. Online blind deconvolution for astronomical imaging[C]//2009 IEEE International Conference on Computational Photography (ICCP), San Francisco, 2009: 1–7. https://doi.org/10.1109/ICCPHOT.2009.5559014. Google Scholar
[17]	Hirsch M, Harmeling S, Sra S, et al. Online multi-frame blind deconvolution with super-resolution and saturation correction[J]. Astron Astrophys, 2011, 531: A9. doi: 10.1051/0004-6361/200913955 CrossRef Google Scholar
[18]	Fergus R, Singh B, Hertzmann A, et al. Removing camera shake from a single photograph[C]//ACM SIGGRAPH 2006 Papers, Massachusetts, 2006: 787–794. https://doi.org/10.1145/1179352.1141956. Google Scholar
[19]	Babacan S D, Molina R, Do M N, et al. Bayesian blind deconvolution with general sparse image priors[C]//Proceedings of the 12th European Conference on Computer Vision, Florence, 2012: 341–355. https://doi.org/10.1007/978-3-642-33783-3_25. Google Scholar
[20]	Levin A, Weiss Y, Durand F, et al. Efficient marginal likelihood optimization in blind deconvolution[C]//CVPR 2011, Colorado Springs, 2011: 2657–2664. https://doi.org/10.1109/CVPR.2011.5995308. Google Scholar
[21]	Levin A, Weiss Y, Durand F, et al. Understanding blind deconvolution algorithms[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 33(12): 2354−2367. doi: 10.1109/TPAMI.2011.148 CrossRef Google Scholar
[22]	Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure[C]//CVPR 2011, Colorado Springs, 2011: 233–240. https://doi.org/10.1109/CVPR.2011.5995521. Google Scholar
[23]	Xu L, Zheng S C, Jia J Y. Unnatural l0 sparse representation for natural image deblurring[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013: 1107–1114. https://doi.org/10.1109/CVPR.2013.147. Google Scholar
[24]	Sun L B, Cho S, Wang J, et al. Edge-based blur kernel estimation using patch priors[C]//IEEE International Conference on Computational Photography (ICCP), Cambridge, 2013: 1–8. https://doi.org/10.1109/ICCPhot.2013.6528301. Google Scholar
[25]	Michaeli T, Irani M. Blind deblurring using internal patch recurrence[C]//Proceedings of the 13th European Conference on Computer Vision, Zurich, 2014: 783–798. https://doi.org/10.1007/978-3-319-10578-9_51. Google Scholar
[26]	Pan J S, Hu Z, Su Z X, et al. Deblurring text images via L0-regularized intensity and gradient prior[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2901–2908. https://doi.org/10.1109/CVPR.2014.371. Google Scholar
[27]	Pan J S, Liu R S, Su Z X, et al. Motion blur kernel estimation via salient edges and low rank prior[C]//2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, 2014: 1–6. https://doi.org/10.1109/ICME.2014.6890182. Google Scholar
[28]	Ren W Q, Cao X C, Pan J S, et al. Image deblurring via enhanced low-rank prior[J]. IEEE Trans Image Process, 2016, 25(7): 3426−3437. doi: 10.1109/TIP.2016.2571062 CrossRef Google Scholar
[29]	Pan J S, Sun D Q, Pfister H, et al. Blind image deblurring using dark channel prior[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016: 1628–1636. https://doi.org/10.1109/CVPR.2016.180. Google Scholar
[30]	Cho S, Lee S. Fast motion deblurring[C]//ACM SIGGRAPH Asia 2009 Papers, Yokohama, 2009: 145. https://doi.org/10.1145/1661412.1618491. Google Scholar
[31]	Xu L, Jia J Y. Two-phase kernel estimation for robust motion deblurring[C]//Proceedings of the 11th European Conference on Computer Vision, Heraklion, 2010: 157–170. https://doi.org/10.1007/978-3-642-15549-9_12. Google Scholar
[32]	Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Phys D: Nonlinear Phenom, 1992, 60(1–4): 259−268. doi: 10.1016/0167-2789(92)90242-F CrossRef Google Scholar
[33]	Perrone D, Favaro P. Total variation blind deconvolution: the devil is in the details[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2909–2916. https://doi.org/10.1109/CVPR.2014.372. Google Scholar
[34]	Bredies K, Kunisch K, Pock T. Total generalized variation[J]. SIAM J Imag Sci, 2010, 3(3): 492−526. doi: 10.1137/090769521 CrossRef Google Scholar
[35]	许建楼, 冯象初, 郝岩. 自适应二阶总广义变分图像恢复方法[J]. 光电子·激光, 2013, 24(2): 378−383. doi: 10.16136/j.joel.2013.02.028 CrossRef Google Scholar Xu J L, Feng X C, Hao Y. Image restoration method with adaptive second order total generalized variation[J]. J Optoelectron·Laser, 2013, 24(2): 378−383. doi: 10.16136/j.joel.2013.02.028 CrossRef Google Scholar
[36]	Shao W Z, Wang F, Huang L L. Adapting total generalized variation for blind image restoration[J]. Multidimens Syst Signal Process, 2019, 30(2): 857−883. doi: 10.1007/s11045-018-0586-0 CrossRef Google Scholar
[37]	Zhang X X, Wang R G, Tian Y H, et al. Image deblurring using robust sparsity priors[C]//2015 IEEE International Conference on Image Processing (ICIP), Quebec City, 2015: 138–142. https://doi.org/10.1109/ICIP.2015.7350775. Google Scholar
[38]	Xu L, Lu C W, Xu Y, et al. Image smoothing via L₀ gradient minimization[C]//Proceedings of the 2011 SIGGRAPH Asia Conference, Hong Kong, China, 2011: 174. https://doi.org/10.1145/2024156.2024208. Google Scholar
[39]	Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors[C]//Proceedings of the 22nd International Conference on Neural Information Processing Systems, Vancouver, 2009: 1033–1041. Google Scholar
[40]	Liu R T, Jia J Y. Reducing boundary artifacts in image deconvolution[C]//2008 15th IEEE International Conference on Image Processing, San Diego, 2008: 505–508. https://doi.org/10.1109/ICIP.2008.4711802. Google Scholar
[41]	Zhong L B, Tian Y, Rao C H. The speckle image reconstruction of the solar small scale features[J]. Proc SPIE, 2014, 9301: 93012X. doi: 10.1117/12.2073104 CrossRef Google Scholar
[42]	张姣, 李俊山, 杨亚威. 结合仿射变换和多层B样条配准的湍流畸变图像校正[J]. 光学精密工程, 2015, 23(3): 846−854. doi: 10.3788/OPE.20152303.0846 CrossRef Google Scholar Zhang J, Li J S, Yang Y W. Turbulence distorted image correction using affine transformation and multilevel B-spline registration[J]. Opt Precis Eng, 2015, 23(3): 846−854. doi: 10.3788/OPE.20152303.0846 CrossRef Google Scholar