From the perspective of resolution, radiation damage, price–performance ratio, and other aggregate indicators, optical imaging technology featuring high spatiotemporal resolution, high information capacity, and nonionizing properties have garnered significant research attention in the fields of biomedical and material science.^1,2 Currently, high-resolution imaging technologies, such as fluorescence microscopy (FM),^{3 – 5} confocal microscopy (CM),^6,7 two-photon microcopy (TPM),^{8 – 11} optical coherence tomography (OCT),^12,13 and full-field optical coherence tomography (FF-OCT),^14,15 have been widely adopted. Each technology has its distinct features. Combining fluorescence probe and optical microscopy, FM can perform micron-scale high-resolution imaging of tissues, organoids, cells, and other samples. However, FM cannot provide depth information and exhibits phototoxicity to tissues.^16,17 Based on single-photon or two-photon excitation, CM and TPM render high imaging resolution and images with good contrast, but their imaging depth is mostly limited to a few hundred $\mu \mathrm{m}$.⁹ Based on the principle of low coherent interference, OCT can provide histology-like images of a sample noninvasively; the transverse resolution and axial resolution of OCT can be controlled, respectively, which can reach $10\mu \mathrm{m}$ or even $1\mu \mathrm{m}$; OCT imaging depth can reach 1 mm or higher, according to the scattering characteristics of samples, whereas the system cost is considerably lower than that of TPI. Therefore, OCT has attracted increased attention since it was first proposed in 1991.^12,13,18

In current off-axis system design process, optical path configuration (OPC) of the system often remains unchanged due to the explicit physical constraints added in the optimization process, and this prevents designers from obtaining potential better results with other OPCs. In this paper, we present a method to change the OPC automatically by ray-quadrangle-based optimization. In our method, a vector is utilized to represent the OPC and a penalty function based on the difference between the current vector and its target value is added into the optimization error function. During OPC variation, obscuration can be avoided without human interference. Examples are given as validation of the proposed method.

We consider the problem of hyperspectral image (HSI) reconstruction, which aims to recover 3D hyperspectral data from 2D compressive HSI measurements acquired by a coded aperture snapshot spectral imaging (CASSI) system. Existing deep learning methods have achieved acceptable results in HSI reconstruction. However, these methods did not consider the imaging system degradation pattern. In this paper, based on observing the initialized HSIs obtained by shifting and splitting the measurements, we propose a dynamic Fourier network based on degradation learning, called the degradation-aware dynamic Fourier-based network (DADF-Net). We estimate the degradation feature maps from the degraded hyperspectral images to realize the linear transformation and dynamic processing of the features. In particular, we use the Fourier transform to extract the HSI non-local features. Extensive experimental results show that the proposed model outperforms state-of-the-art algorithms on simulation and real-world HSI datasets. The source code is available at: https://github.com/CISMOLab/DADF-Net

In this paper, we propose an off-axis reflective system design method based on a non-rotational symmetric conicoid-based freeform (CBF) surface description. The base description avoids complicated calculation of decenter and tilt when using the conventional conic expression, thus simplify the system modeling and optimization process, and it can reduce the number of coefficients that needed to represent mild freeform surfaces. A design method that includes the automatic initial system searching, preliminary optimization with rotationally symmetric surface deviation and fine-tuning with non-symmetric surface deviation is proposed. Two three-mirror systems have been designed to demonstrate the feasibility and conveniences of the proposed method.

As we know, most natural cells are transparent in intensity images. Thus, stains or fluorescent dyes have been used to help visualize them. But, for some cases, such as living cultured cells, it is different to stain them. Moreover, long-term observation on fluorescent sample will cause photobleaching and phototoxicity. Phase imaging can ease these problems. For example, with the differential interference contrast microscopy,¹ label-free living cells can be easily monitored. Nevertheless, this kind of method only records qualitative phase information. Digital holographic microscopy (DHM) can record quantitative phase information,^2,3 with high sensitivity, accuracy, and resolution, in a noninvasive way. But, in this method, the sample phase is often embedded within aberrations, highly limiting its applications. Fortunately, these aberrations can be quantified and then compensated with physical or numerical methods.^{4 – 8}