基于扩展标量衍射理论，建立了衍射光学元件的微结构高度与周期宽度和入射角度的理论关系模型，提出了不同入射角度时，利用带宽积分平均衍射效率最大化实现设计波长和微结构高度等结构参数的优化设计方法。以工作在近红外波段的衍射光学元件为例进行分析。结果表明：周期宽度一定时，入射角度的改变会引起基于带宽积分平均衍射效率最大化所确定的结构参数发生变化。该设计方法和结论可以用于指导衍射光学元件的设计。

Information retrieval from visually random optical speckle patterns is desired in many scenarios yet considered challenging. It requires accurate understanding or mapping of the multiple scattering process, or reliable capability to reverse or compensate for the scattering-induced phase distortions. In whatever situation, effective resolving and digitization of speckle patterns are necessary. Nevertheless, on some occasions, to increase the acquisition speed and/or signal-to-noise ratio (SNR), speckles captured by cameras are inevitably sampled in the sub-Nyquist domain via pixel binning (one camera pixel contains multiple speckle grains) due to finite size or limited bandwidth of photosensors. Such a down-sampling process is irreversible; it undermines the fine structures of speckle grains and hence the encoded information, preventing successful information extraction. To retrace the lost information, super-resolution interpolation for such sub-Nyquist sampled speckles is needed. In this work, a deep neural network, namely SpkSRNet, is proposed to effectively up sample speckles that are sampled below 1/10 of the Nyquist criterion to well-resolved ones that not only resemble the comprehensive morphology of original speckles (decompose multiple speckle grains from one camera pixel) but also recover the lost complex information (human face in this study) with high fidelity under normal- and low-light conditions, which is impossible with classic interpolation methods. These successful speckle super-resolution interpolation demonstrations are essentially enabled by the strong implicit correlation among speckle grains, which is non-quantifiable but could be discovered by the well-trained network. With further engineering, the proposed learning platform may benefit many scenarios that are physically inaccessible, enabling fast acquisition of speckles with sufficient SNR and opening up new avenues for seeing big and seeing clearly simultaneously in complex scenarios.

相移轮廓术是一种广泛使用的光学三维测量方法，其精度不仅受相位展开算法本身的影响，也受测量系统中投影仪和摄像机的非线性影响。理论上，投射更多的相移条纹可减弱非线性误差的影响，但是增加了测量时间。为了提高误差校正的效率，提出了一种基于梯形正弦相移的测量方法。该方法需要两组改进的梯形相移条纹和一幅正弦条纹。梯形条纹提供图像强度信息和条纹级次信息，图像强度信息用来求取系统的非线性响应曲线，进一步消除系统的非线性。正弦条纹经过希尔伯特变换可求得额外的条纹图像，用来计算截断相位信息。经过校正的截断相位信息，可进一步获取精度较高的三维信息。相较于先前的梯形与正弦误差校正方法，该方法的测量效率提高了28%。

A tunable multi-wavelength erbium-doped fiber laser with precise wavelength interval control is reported theoretically and experimentally in this paper. It is made up of a Mach–Zehnder interferometer (MZI) filter and a Sagnac filter and supplemented by the four-wave-mixing effect. Compared with other filters, the proposed MZI filter based on the fused taper technology can change the wavelength interval more flexibly. The experiment result shows that wavelength tuning can be achieved, and the tuning range can reach ∼15nm. Moreover, the variation in the number of wavelengths is also realized. The maximum side-mode suppression ratio can reach 39 dB.