高速成像技术在物理、化学、生物医学、材料科学及工业等众多领域扮演着十分重要的角色。受电荷存储和读取速度的限制，基于电子成像器件的数码相机成像速度难以进一步提高。近年来，随着成像新技术的发展，超高速和极高速光学成像的性能已得到显著提升，具备更高的时间分辨率、空间分辨率及更大的序列深度等。介绍高速成像技术的发展历程，根据成像方式，将近年来具有代表性的新型超高速和极高速光学成像技术分为直接成像和编码计算成像两个类别。分别介绍和讨论各种新型超高速和极高速光学成像技术的概念和原理，并比较各自的优缺点。最后，对这一领域的发展趋势和前景进行展望。本文旨在帮助研究者系统了解超高速和极高速光学成像技术的基本知识、最新研究发展趋势和潜在应用，为该领域科学研究提供参考。

The rapid development of neuromorphic computing has stimulated extensive research interest in artificial synapses. Optoelectronic artificial synapses using laser beams as stimulus signals have the advantages of broadband, fast response, and low crosstalk. However, the optoelectronic synapses usually exhibit short memory duration due to the low lifetime of the photo-generated carriers. It greatly limits the mimicking of human perceptual learning, which is a common phenomenon in sensory interactions with the environment and practices of specific sensory tasks. Herein, a heterostructure optoelectronic synapse based on graphene nanowalls and CsPbBr3 quantum dots was fabricated. The graphene/CsPbBr3 heterojunction and the natural middle energy band in graphene nanowalls extend the carrier lifetime. Therefore, a long half-life period of photocurrent decay - 35.59 s has been achieved. Moreover, the long-term optoelectronic response can be controlled by the adjustment of numbers, powers, wavelengths, and frequencies of the laser pulses. Next, an artificial neural network consisting of a 28 × 28 synaptic array was established. It can be used to mimic a typical characteristic of human perceptual learning that the ability of sensory systems is enhanced through a learning experience. The learning behavior of image recognition can be tuned based on the photocurrent response control. The accuracy of image recognition keeps above 80% even under a low-frequency learning process. We also verify that less time is required to regain the lost sensory ability that has been previously learned. This approach paves the way toward high-performance intelligent devices with controllable learning of visual perception.

由于超材料和超表面的亚波长结构单元的形状和尺寸具有很大的设计自由度,可对电磁波的振幅、相位、波前和方向等进行复杂而精确的调控,同时随着结构参数数量的增加,结构设计的时间往往呈指数增长。提出了一种基于反向传播(BP)神经网络快速优化超表面结构的方法,实现了兼具高衍射效率、宽带宽和高角色散等优势的太赫兹介质超光栅。利用有限次数的严格耦合波分析建立的数据集来训练BP神经网络,可准确预测任意结构参数的超光栅衍射光谱,并通过遍历所有结构参数快速筛选出具有最高衍射效率且宽带宽的超光栅,相比传统的遍历计算方法速度提高了一万倍,证明了基于BP神经网络的超表面优化方法的高效性以及精准性,同时为太赫兹波段提供了一种性能优异的衍射元件。

Bessel beam propagation in scattering media is simulated using the angular spectrum method combined with slice-by-slice propagation model. Generating Bessel beams with a spatial light modulator, which provides a means to adjust flexibly the parameters of the Bessel beam, allows us to validate the simulation results experimentally. The study reveals that the self-reconstructing length changes oppositely with the axicon angle (i.e., the larger the axicon angle, the shorter the self-reconstructing length). The radius of the incident beam has little influence on the self-reconstruction of the Bessel beam central lobe.