Structure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algorithm is prone to artifacts due to the high signal-to-noise ratio (SNR) requirement, and existing deep-learning SIM algorithms still have the potential to improve imaging speed. Here, we introduce a deep-learning-based video-level and high-fidelity super-resolution SIM reconstruction method, termed video-level deep-learning SIM (VDL-SIM), which has an imaging speed of up to 47 frame/s, providing a favorable observing experience for users. In addition, VDL-SIM can robustly reconstruct sample details under a low-light dose, which greatly reduces the damage to the sample during imaging. Compared with existing SIM algorithms, VDL-SIM has faster imaging speed than existing deep-learning algorithms, and higher imaging fidelity at low SNR, which is more obvious for traditional algorithms. These characteristics enable VDL-SIM to be a useful video-level super-resolution imaging alternative to conventional methods in challenging imaging conditions.

为缩短12寸晶圆检测成像系统的轴向和径向尺寸，提出一种小角度棱镜折转光路与超短物像距镜头相结合的解决方法。设计优于1/12λ（λ=632.8 nm）面形精度的小角度棱镜折转光路，实现照明系统与成像镜头的水平布置，径向尺寸仅为80 mm，在保证不影响系统成像质量的前提下，极大地降低了整个系统的径向尺寸，同时也实现了12°的小角度明场照明。设计放大倍率为0.264的对称混合型光学系统，采用纯球面系统获得较大成像视场，像高为81.92 mm，物像距仅为392.5 mm，极大地降低了整个系统轴向尺寸。设计结果表明，整个成像系统全视场平均光学传递函数优于0.4@100l p/mm，相对畸变优于0.03%，像面照度均匀性全视场优于50%。实际测试结果表明：全视场实际成像分辨率优于18.88 μm，达到了系统极限分辨率；全视场像面照度均匀性为43.3%，满足均匀性优于40%的研制要求。研究结果表明本文提出的超薄超短物像距高分辨率检测成像系统合理、有效，解决了12寸晶圆检测成像系统空间尺寸压缩的难题，并降低了研制成本，为后续近距离大尺寸物体检测成像系统的研制提供参考依据。

本文提出了一种基于Micro LED阵列的车灯投影方案，设计了以像素尺寸为80 μm×80 μm的200×150白光Micro LED阵列作为显示光源，视场角为16°×34°的车灯投影光学系统，并对物面倾斜角度和光学系统结构进行了优化。此外，分别采用反向畸变处理方法和像素灰度调制方法用以解决车灯投影图像的梯形畸变和照度均匀性问题，并搭建了投影实验平台，对图像校正方法进行了验证。实验结果表明：校正后图像梯形畸变系数p₁，p₂分别从0.0932和0.3680下降至0.0835和0.0373，像面照度均匀性从83.2%提高到93.2%。本文通过对基于Micro LED的倾斜投影车灯光学系统进行优化设计及采用图像校正方法，实现了高光效、低畸变的车灯投影。

细胞内镜需实现最大倍率约500倍的连续放大成像，受光纤照明及杂散光的影响，其图像存在不均匀光照，且光照分布会随放大倍率的变化而变化。这会影响医生对病灶的观察及判断。为此，本文提出一种基于细胞内镜光照模型的图像不均匀光照校正算法。根据图像信息由光照分量和反射分量组成这一基础，该算法通过卷积神经网络学习图像的光照分量，并基于二维Gamma函数实现不均匀光照校正。实验表明，经本文方法进行不均匀光照校正后，图像的光照分量平均梯度和离散熵分别为0.22和7.89，优于自适应直方图均衡化、同态滤波和单尺度Retinex等传统方法以及基于深度学习的WSI-FCN算法。

Wide-field linear structured illumination microscopy (LSIM) extends resolution beyond the diffraction limit by moving unresolvable high-frequency information into the passband of the microscopy in the form of moiré fringes. However, due to the diffraction limit, the spatial frequency of the structured illumination pattern cannot be larger than the microscopy cutoff frequency, which results in a twofold resolution improvement over wide-field microscopes. This Letter presents a novel approach in point-scanning LSIM, aimed at achieving higher-resolution improvement by combining stimulated emission depletion (STED) with point-scanning structured illumination microscopy (psSIM) (STED-psSIM). The according structured illumination pattern whose frequency exceeds the microscopy cutoff frequency is produced by scanning the focus of the sinusoidally modulated excitation beam of STED microscopy. The experimental results showed a 1.58-fold resolution improvement over conventional STED microscopy with the same depletion laser power.