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| 1 | A review of common-path off-axis digital holography:towards high stable optical instrument manufacturing显示文摘Digital holography possesses the advantages of wide-field,non-contact,precise,and dynamic measurements for the complex amplitude of object waves.Today,digital holography and its derivatives have been widely applied in interferometric measurements,three-dimensional imaging,and quantitative phase imaging,demonstrating significant potential in the material science,industry,and biomedical fields,among others.However,in conventional off-axis holographic experimental setups,the object and reference beams propagate in separated paths,resulting in low temporal stability and measurement sensitivity.By designing common-path configurations where the two interference beams share the same or similar paths,environmental disturbance to the two beams can be effectively compensated.Therefore,the temporal stability of the experimental setups for hologram recording can be significantly improved for time-lapsing measurements.In this review,we categorise the common-path models as lateral shearing,point diffraction,and other types based on the different approaches to generate the reference beam.Benefiting from compact features,common-path digital holography is extremely promising for the manufacture of highly stable optical measurement and imaging instruments in the future. | Jiwei Zhang Siqing Dai Chaojie Ma Teli Xi Jianglei Di Jianlin Zhao | 2021 | Light(Advanced Manufacturing)2021，2，3： | 4 |
| 2 | Digital color holographic recording and reconstruction using synthetic aperture and multiple reference waves显示文摘 | Jiang Hongzhen Zhao Jianlin Di Jianglei | 2012 | Opt Commun2012，13，5： | 1 |
| 3 | Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics显示文摘The time-delay problem,which is introduced by the response time of hardware for correction,is a critical and nonignorable problem of adaptive optics(AO)systems.It will result in significant wavefront correction errors while turbulence changes severely or system responses slowly.Predictive AO is proposed to alleviate the time-delay problem for more accurate and stable corrections in the real time-varying atmosphere.However,the existing prediction approaches either lack the ability to extract non-linear temporal features,or overlook the authenticity of spatial features during prediction,leading to poor robustness in generalization.Here,we propose a mixed graph neural network(MGNN)for spatiotemporal wavefront prediction.The MGNN introduces the Zernike polynomial and takes its inherent covariance matrix as physical constraints.It takes advantage of conventional convolutional layers and graph convolutional layers for temporal feature catch and spatial feature analysis,respectively.In particular,the graph constraints from the covariance matrix and the weight learning of the transformation matrix promote the establishment of a realistic internal spatial pattern from limited data.Furthermore,its prediction accuracy and robustness to varying unknown turbulences,including the generalization from simulation to experiment,are all discussed and verified.In experimental verification,the MGNN trained with simulated data can achieve an approximate effect of that trained with real turbulence.By comparing it with two conventional methods,the demonstrated performance of the proposed method is superior to the conventional AO in terms of root mean square error(RMS).With the prediction of the MGNN,the mean and standard deviation of RMS in the conventional AO are reduced by 54.2%and 58.6%at most,respectively.The stable prediction performance makes it suitable for wavefront predictive correction in astronomical observation,laser communication,and microscopic imaging. | JU TANG JI WU JIAWEI ZHANG MENGMENG ZHANG ZHENBO REN JIANGLEI DI LIUSEN HU GUODONG LIU JIANLIN ZHAO | 2023 | Photonics Research2023，11，11： | 0 |
| 4 | On the use of deep learning for phase recovery显示文摘Phase recovery(PR)refers to calculating the phase of the light field from its intensity measurements.As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics,PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system.In recent years,deep learning(DL),often implemented through deep neural networks,has provided unprecedented support for computational imaging,leading to more efficient solutions for various PR problems.In this review,we first briefly introduce conventional methods for PR.Then,we review how DL provides support for PR from the following three stages,namely,pre-processing,in-processing,and post-processing.We also review how DL is used in phase image processing.Finally,we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR.Furthermore,we present a live-updating resource(http://gffzz188fe103f8f1460asopovk669kn096q5f.ffgz.tsg.suse.edu.cn/kqwang/phase-recovery)for readers to learn more about PR. | Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao Edmund Y.Lam | 2024 | Light(Science & Applications)2024，13，2： | 0 |
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