Acta Optica Sinica, Volume. 42, Issue 20, 2011002(2022)
Ultrafast Imaging Based on Polarization Encoding
Among the current single-shot ultrafast imaging methods, the direct imaging methods have high resolutions but complex detection systems, while the computational ones have simple detection systems but easily impaired spatial resolutions. Therefore, ultrafast imaging based on polarization encoding is proposed. This imaging system uses a half-wave plate array and a polarizer array to achieve polarization encoding of the incident femtosecond pulses, emergent femtosecond pulses and dynamic events, and it decodes the ultrafast dynamic time-series images by linear simultaneous equations. Then, several images are accurately restored by constructing an optical model and conducting simulations, and the feasibility of the proposed scheme is verified. In theory, its framing rate can reach more than 1013 frame/s, and its intrinsic spatial resolution can be as high as 114 lp/mm. The proposed imaging system combines the advantages of direct and computational imaging systems. Specifically, the results of linear simultaneous equations are accurate, which frees the optical system from resolution impairments. Besides, the superposition of time-series images allows the detection structure to split beams alone, thereby saving the effort of dividing the images of different moments spatially and ultimately simplifying the detection structure. The temporal resolution of the proposed ultrafast imaging system is only constrained by pulse width. This system can be used to achieve the detection of femtosecond dynamic events, and its temporal resolution can be further enhanced by narrowing pulse width.
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Qifan Zhu, Yi Cai, Xuanke Zeng, Hu Long, Liangwei Zeng, Yongle Zhu, Jingzhen Li, Xiaowei Lu. Ultrafast Imaging Based on Polarization Encoding[J]. Acta Optica Sinica, 2022, 42(20): 2011002
Category: Imaging Systems
Received: Mar. 1, 2022
Accepted: May. 10, 2022
Published Online: Oct. 18, 2022
The Author Email: Li Jingzhen (lijz@szu.edu.cn), Lu Xiaowei (xiaoweilu@szu.edu.cn)