The model fitting degree of optical freeform surfaces is of utmost design importance. We develop a model with radial basis functions based on the surface slope (RBF-slope) for optical freeform surfaces with asymmetric structures. The RBF-slope model improves the basis-function distribution for circular apertures and establishes a relationship between shape factor and local surface slope, which provides the model with better fitting ability than the conventional RBF model (RBF-direct); fitting experiments for off-axis conic surfaces, “bumpy” paraboloids, and the design of a single mirror magnifier demonstrate the efficacy of our approach. Our method can effectively improve aberration balancing of optical freeform surfaces, resulting in high-quality imaging.

An approximate voxel model for integral imaging is proposed by ray tracing. By analyzing the case of corresponding pixels overlapping completely and partially in the image space, the voxel is defined with an appropriate approximation, and the voxel size and its distribution feature in imaging space are derived. The model is verified in a reconstruction experiment of a resolution target and compared with the calculation result of an integral imaging display or reconstruction system. The proposed model is simple and easy to calculate and thus useful for the evaluation and optimization of integral imaging systems.

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Light field imaging has shown significance in research fields for its high-temporal-resolution 3D imaging ability. However, in scenes of light field imaging through scattering, such as biological imaging in vivo and imaging in fog, the quality of 3D reconstruction will be severely reduced due to the scattering of the light field information. In this paper, we propose a deep learning-based method of scattering removal of light field imaging. In this method, a neural network, trained by simulation samples that are generated by light field imaging forward models with and without scattering, is utilized to remove the effect of scattering on light fields captured experimentally. With the deblurred light field and the scattering-free forward model, 3D reconstruction with high resolution and high contrast can be realized. We demonstrate the proposed method by using it to realize high-quality 3D reconstruction through a single scattering layer experimentally.

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.