| Citation: | ZHUO Yue, MENG Qing-yu, SUN Tian-yu, YAN Shu-run, GUO Xiao-tong, KANG Ze-feng. Physics-informed computational aberration correction for simplified optical systems[J]. Chinese Optics. doi: 10.37188/CO.2025-0144
|
To address the issues of structural complexity and high cost in high-performance optical systems, this study proposes an optical system simplification and aberration correction method oriented toward computational correction. On the optical design side, a simplification design criterion based on aberration correctability analysis is constructed: priority is given to suppressing aberrations that are difficult for neural networks to compensate, while retaining portions amenable to computational correction, thereby simplifying the optical system structure while ensuring imaging quality. On the computational processing side, a multi-module progressive collaborative correction network is designed, comprising four modules: distortion correction, chromatic aberration compensation, monochromatic aberration correction based on physically-constrained Point Spread Function, and frequency-domain enhancement. This network is driven by a Temporal Stage Controller (TSC), which utilizes its dynamic weight scheduling mechanism for progressive stage-wise processing, effectively suppressing the mutual interference between different aberration types. Experimental results demonstrate that images from a simplified dual-lens system corrected by this network achieve a Peak Signal-to-Noise Ratio (PSNR) of 31.47 dB and Structural Similarity (SSIM) of 0.95, with imaging quality comparable to conventional six-lens double-Gauss systems, while significantly reducing optical system complexity. Ablation studies validate the effectiveness of the TSC and multi-module correction architecture. This research provides a novel technical pathway for achieving high-quality imaging with simplified optical systems.
| [1] |
王慎, 刘泉, 国成立, 等. 基于计算全息图零位补偿的同轴高次非球面干涉检测技术研究[J]. 中国光学(中英文), 2025, 18(2): 237-244. doi: 10.37188/CO.2024-0152
WANG SH, LIU Q, GUO CH L, et al. CGH null compensation testing of high-order coaxial aspherical surfaces[J]. Chinese Optics, 2025, 18(2): 237-244. (in Chinese). doi: 10.37188/CO.2024-0152
|
| [2] |
许恒深, 姜玉婷, 胡跃强. 高性能几何光波导头戴显示器设计与量产制造[J]. 光学 精密工程, 2025.
XU H SH, JIANG Y T, HU Y Q. Design and mass production of high-performance geometric waveguide head-mounted displays[J]. Optics and Precision Engineering, 2025. (in Chinese) (查阅网上资料, 未找到本条文献卷期和页码信息, 请确认).
|
| [3] |
王翘楚, 耿海涛, 虞林瑶, 等. 红外双波段制冷型变焦Offner型光谱成像系统设计[J]. 中国光学(中英文), 2025, 18(6): 1327-1343. doi: 10.37188/CO.2025-0080
WANG Q CH, GENG H T, YU L Y, et al. Design of an infrared dual-band cooled zoom focal Offner-type spectral imaging optical system[J]. Chinese Optics, 2025, 18(6): 1327-1343. (in Chinese). doi: 10.37188/CO.2025-0080
|
| [4] |
吴寅, 王跃明, 张东. 星载全谱段高光谱系统设计[J]. 中国光学(中英文), 2025, 18(2): 368-375. doi: 10.37188/CO.2024-0150
WU Y, WANG Y M, ZHANG D. Design of spaceborne full-spectrum hyperspectral system[J]. Chinese Optics, 2025, 18(2): 368-375. (in Chinese). doi: 10.37188/CO.2024-0150
|
| [5] |
魏雅喆. 计算成像中光学联合设计方法研究[D]. 西安: 西安电子科技大学, 2019.
WEI Y ZH. Research on joint optics-image design in computational imaging[D]. Xi’an: Xidian University, 2019. (in Chinese).
|
| [6] |
王洋, 彭圣博, 顾志远. 含自由曲面的反射式望远镜降敏设计[J]. 光学精密工程, 2025, 33(12): 1864-1875.
WANG Y, PENG SH B, GU ZH Y. Desensitization design methods for reflective telescopes with freeform surfaces[J]. Optics and Precision Engineering, 2025, 33(12): 1864-1875. (in Chinese).
|
| [7] |
刘新宇, 陈雅婷, 吴佳琛, 等. 计算光谱成像系统及光谱重建算法[J]. 光学精密工程, 2026, 34(1): 1-25.
LIU X Y, CHEN Y T, WU J CH, et al. Computational spectral imaging systems and reconstruction algorithms[J]. Optics and Precision Engineering, 2026, 34(1): 1-25. (in Chinese).
|
| [8] |
SCHULER C J, HIRSCH M, HARMELING S, et al. Non-stationary correction of optical aberrations[C]. 2011 International Conference on Computer Vision, IEEE, 2011: 659-666.
|
| [9] |
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. doi: 10.1145/2516971.2516974
|
| [10] |
郑云达, 黄玮, 徐明飞, 等. 大视场高像质简单光学系统的光学-算法协同设计[J]. 中国光学(中英文), 2019, 12(5): 1090-1099. doi: 10.3788/CO.20191205.1090
ZHENG Y D, HUANG W, XU M F, et al. Optical/algorithmic co-design of large-field high-quality simple optical system[J]. Chinese Optics, 2019, 12(5): 1090-1099. (in Chinese). doi: 10.3788/CO.20191205.1090
|
| [11] |
CHEN SH Q, FENG H J, PAN D X, et al. Optical aberrations correction in postprocessing using imaging simulation[J]. ACM Transactions on Graphics (TOG), 2021, 40(5): 192. doi: 10.1145/3474088
|
| [12] |
PENG Y F, SUN Q L, DUN X, et al. Learned large field-of-view imaging with thin-plate optics[J]. ACM Transactions on Graphics (TOG), 2019, 38(6): 219. doi: 10.1145/3355089.3356526
|
| [13] |
NIE Y F, SU R M, ZHANG J G, et al. End-to-end aberration correction network for enhancing miniature microscope resolution[J]. Optics and Lasers in Engineering, 2025, 184(Pt 1): 108558.
|
| [14] |
LI X, SUO J L, ZHANG W H, et al. Universal and flexible optical aberration correction using deep-prior based deconvolution[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, IEEE, 2021: 2593-2601.
|
| [15] |
RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]. Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Springer, 2015: 234-241.
|
| [16] |
ZHOU J W, CHEN SH Q, REN ZH, et al. Revealing the preference for correcting separated aberrations in joint optic-image design[J]. Optics and Lasers in Engineering, 2024, 178: 108220. doi: 10.1016/j.optlaseng.2024.108220
|
| [17] |
WANG Y H, ZHONG X, QU ZH, et al. Simplified design method for optical imaging systems based on aberration characteristics of optical-digital joint optimization[J]. Applied Optics, 2024, 63(4): 1066-1078. doi: 10.1364/AO.510746
|
| [18] |
THOMPSON K. Description of the third-order optical aberrations of near-circular pupil optical systems without symmetry[J]. Journal of the Optical Society of America A, 2005, 22(7): 1389-1401. doi: 10.1364/JOSAA.22.001389
|
| [19] |
SASIÁN J. Introduction to Aberrations in Optical Imaging Systems[M]. Cambridge: Cambridge University Press, 2012.
|
| [20] |
LIU Y K, ZHANG CH Y, KOU T D, et al. End-to-end computational optics with a singlet lens for large depth-of-field imaging[J]. Optics Express, 2021, 29(18): 28530-28548. doi: 10.1364/OE.433067
|
| [21] |
JI J R, XIE H B, YANG L. Learned large field-of-view imager with a simple spherical optical module[J]. Optics Communications, 2023, 526: 128918. doi: 10.1016/j.optcom.2022.128918
|
| [22] |
ZHOU J W, REN ZH, CHEN B K, et al. Optical aberration correction of lightweight lenses with one unified paradigm[J]. Optics & Laser Technology, 2025, 188: 112906. doi: 10.1016/j.optlastec.2025.112906
|
| [23] |
WANG Z Y, SHI R ZH, ZHOU Y, et al. Enhanced aberration correction in minimalist optical systems with the deep attention Wiener network[J]. Applied Optics, 2025, 64(8): 1924-1932. doi: 10.1364/AO.547094
|
| [24] |
HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[C]. Proceedings of the 34th International Conference on Neural Information Processing Systems, Curran Associates Inc. , 2020: 574.
|
| [25] |
KUPYN O, MARTYNIUK T, WU J R, et al. DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, IEEE, 2019: 8877-8886.
|
| [26] |
ZAMIR S W, ARORA A, KHAN S, et al. Multi-stage progressive image restoration[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2021: 14816-14826.
|
| [27] |
WANG C L, CHEN N, HEIDRICH W. dO: a differentiable engine for deep lens design of computational imaging systems[J]. IEEE Transactions on Computational Imaging, 2022, 8: 905-916. doi: 10.1109/TCI.2022.3212837
|
| [28] |
YANG X G, FU Q, HEIDRICH W. Curriculum learning for ab initio deep learned refractive optics[J]. Nature Communications, 2024, 15(1): 6572. doi: 10.1038/s41467-024-50835-7
|
Figures(12) / Tables(4)
DownLoad:
