Quantitative phase imaging (QPI) has emerged as a promising label-free imaging technique with growing importance in biomedical research, optical metrology, materials science, and other fields. Partially coherent illumination provides resolution twice that of the coherent diffraction limit, along with improved robustness and signal-to-noise ratio, making it an increasingly significant area of study in QPI. Partially coherent QPI, represented by differential phase contrast (DPC), linearizes the phase-to-intensity transfer process under the weak object approximation (WOA). However, the nonlinear errors caused by WOA in DPC can lead to phase underestimation. Additionally, DPC requires strict matching of the illumination numerical aperture (NA) to ensure the complete transmission of low-frequency information. This necessitates precise alignment of the optical system and limits the flexible use of objective and illumination. In this study, the applicability of the WOA under different coherence parameters is explored, and a method to defy WOA by reducing the illumination NA is proposed. The proposed method uses the transport-of-intensity equation through an additional defocused intensity image to recover the lost low-frequency information due to illumination mismatch, without requiring any iterative procedure. This method overcomes the limitations of DPC being unable to recover large phase objects and does not require the strict illumination matching conditions. The accurate quantitative morphological characterization of customized artifact and microlens arrays that do not satisfy WOA under non-matched-illumination conditions demonstrated the precise quantitative capability of the proposed method and its excellent performance in the field of measurement. Meanwhile, the phase retrieval of tongue slices and oral epithelial cells demonstrated its application potential in the biomedical field. The ability to accurately recover phase under a concise and implementable optical setup makes it a promising solution for widespread application in various label-free imaging domains.

Optical diffraction tomography (ODT) is a promising label-free imaging method capable of quantitatively measuring the three-dimensional (3D) refractive index distribution of transparent samples. In recent years, partially coherent ODT (PC-ODT) has attracted increasing attention due to its system simplicity and absence of laser speckle noise. Quantitative phase imaging (QPI) technologies represented by Fourier ptychographic microscopy (FPM), differential phase contrast (DPC) imaging and intensity diffraction tomography (IDT) need to collect several or hundreds of intensity images, which usually introduce motion artifacts when shooting fast-moving targets, leading to a decrease in image quality. Hence, a quantitative real-time phase microscopy (qRPM) for extended depth of field (DOF) imaging based on 3D single-shot differential phase contrast (ssDPC) imaging method is proposed in this research study. qRPM incorporates a microlens array (MLA) to simultaneously collect spatial information and angular information. In subsequent optical information processing, a deconvolution method is used to obtain intensity stacks under different illumination angles in a raw light field image. Importing the obtained intensity stack into the 3D DPC imaging model is able to finally obtain the 3D refractive index distribution. The captured four-dimensional light field information enables the reconstruction of 3D information in a single snapshot and extending the DOF of qRPM. The imaging capability of the proposed qRPM system is experimental verified on different samples, achieve single-exposure 3D label-free imaging with an extended DOF for 160 µm which is nearly 30 times higher than the traditional microscope system.

In the realm of 3D measurement, photometric stereo excels in capturing high-frequency details but suffers from accumulated errors that lead to low-frequency distortions in the reconstructed surface. Conversely, light field (LF) reconstruction provides satisfactory low-frequency geometry but sacrifices spatial resolution, impacting high-frequency detail quality. To tackle these challenges, we propose a photometric stereoscopic light field measurement (PSLFM) scheme that harnesses the strengths of both methods. We have developed an integrated information acquisition system that requires only a single data acquisition and does not necessitate the light source vectors as input. This system enables uncalibrated multispectral photometric stereo reconstruction using a dense convolutional neural network (DCN). After that, the two reconstruction results are processed by frequency domain filtering, and the processed results are fused according to a certain weight, which can be adaptively determined by the algorithm according to the reconstruction error. Utilizing a light field camera as the sole acquisition device allows for natural alignment of data, mitigating registration errors. Our approach demonstrates effectiveness across both online datasets and laboratory samples, achieving an error of about 10° and lower in uncalibrated scenarios, with notable generalization. In conclusion, the proposed method facilitates single-frame measurement without calibration and exhibits strong robustness, which is expected to exert significant influence in the fields of machine vision, 3D printing and manufacturing, as well as virtual reality and augmented reality.

This paper presents an efficient factorial-design-based method to calculate uncertainty propagation in the triangulation process in stereo-vision photogrammetry. A three-level full factorial numerical simulation experiment is executed by regarding 3D measuring points as responses and image point pairs as factors. Hence, the covariance matrix of the 3D measuring point coordinate can be computed from the responses of the factorial experiment with corresponding weights. The measurement uncertainties in x, y, and z axis directions as well as the principal axis direction are computed from the covariance matrix of the 3D measuring point coordinate. The results calculated by the proposed method are compared with that of the Monte Carlo method, which shows that the proposed method can achieve equivalent precision with less computation cost. The spatial-variant uncertainty distribution of stereo-vision photogrammetry is analysed using the proposed method. Measurement uncertainties under different parameters of stereo-vision photogrammetric system are discussed.

Quantitative phase imaging (QPI) has become a valuable tool in the field of biomedical research due to its ability to quantify refractive index variations of live cells and tissues. For example, three-dimensional differential phase contrast (3D DPC) imaging uses through-focus images captured under different illumination patterns deconvoluted with a computed 3D phase transfer function (PTF) to reconstruct the 3D refractive index. In conventional 3D DPC with semi-circular illumination, partially spatially coherent illumination often diminishes phase contrast, exacerbating inherent noise, and can lead to a large number of zero values in the 3D PTF, resulting in strong low-frequency artifacts and deteriorating imaging resolution. To overcome the above drawbacks, we obtain the conditions for acquiring the optimal 3D PTF based on the analysis of the 3D imaging model and the derivation of the 3D PTF calculation process and propose a 3D DPC microscopy based on optimal annular illumination. The proposed optimal annular illumination pattern minimizes the missing frequency components in the 3D Fourier space, resulting in the best noise-robustness and significantly increased phase contrast. To expedite imaging speed, we utilize a 1/2 annular multiplexed illumination, reducing data acquisition volume by 75%. The 3D refractive index tomography of a simulated 3D phase object, unstained tongue sections, and oral epithelial cells demonstrates that our proposed method achieves the above advantages. In conclusion, we demonstrate a novel 3D DPC microscope that only requires replacing the illumination of a commercial microscope with a programmable LED array. The accurate 3D refractive index tomography and the compactness of the system setup allow the method to play a significant role in the biomedical field.

Precisely sensing the light field direction information plays the essential role in the fields of three-dimensional (3D) imaging, light field sensing, target positioning and tracking, remote sensing, etc. It is thrilling to find that the optical fiber can be used as a sensing component due to its high sensitivity, compact size, and strong resistance to electromagnetic interference. According to the core principle that the few-mode fiber output speckle pattern is sensitive to the change of incident light field direction, the variation characteristics is further investigated in this research study. Based on the simulation and analysis of the fiber transmission characteristics, the output speckle corresponding to the incident light field with the direction in the range of ±6° horizontally and vertically are calculated. Furthermore, a deep convolutional neural network (CNN): fiber speckle demodulation network (FSDNET) is proposed and constructed to establish what we believe to be a novel way to reveal and identify the mapping relationship between the light field direction and the output speckle. The theoretical simulation shows that the mean absolute error (MAE) between the perceived light field directions and the true directions is 0.01°. Then, a light field direction sensing system based on the few-mode fiber is developed. Regarding to the performance of the sensing system, the MAE of the FSDNET for the light field directions that have appeared in the training set is 0.0389°, and for testing set of the unknown directions that have not appeared in the training set, the MAE is 0.0570°. Therefore, the simulation and experimental results prove that high performance sensing of light field direction can be achieved by the proposed few-mode fiber sensing system and the FSDNET.