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We proposed a Wollaston-prism-based snapshot phase-shifting diffraction phase microscope (WP-SPDPM) for low-coherence snapshot quantitative phase imaging and videography. Wollaston prism separates two orthogonally linearly polarized beams with high degrees of polarization at a sufficiently small separation angle; one of the beams passing through a pinhole serves as the reference beam. Four phase-shifted interferograms are simultaneously acquired with a polarization camera to accurately retrieve a high spatial resolution phase map. The system is nearly common-path in configuration and can achieve a large slope range and high accuracy. In addition to the ability to resist environmental noise, the WP-SPDPM is suitable for phase measurement using low-coherence light. The accuracy and large measurable slope range of the proposed system is validated and compared experimentally with a commercial profilometer. We believe WP-SPDPM is a powerful tool for the accurate and robust quantitative phase measurement and has a significant potential of the real-time phase imaging. 

We propose an on-axis deflectometric system for the accurate measurement of freeform surfaces with large slope ranges. A miniature plane mirror is attached on the illumination screen to fold the optical path and achieve the on-axis deflectometric testing. Due to the existence of the miniature folding mirror, the deep-learning method is applied to recover the missing surface data in a single measurement. Low sensitivity to the calibration error of system geometry and high testing accuracy can be achieved with the proposed system. The feasibility and accuracy of the proposed system have been validated. The system is low in cost and simple in configuration, and it provides a feasible way for the flexible and general testing of freeform surfaces, with a significant potential of the application in on-machine testing.

 

On the demand of low-cost, lightweight, miniaturized, and integrated optical systems, precision lenslet arrays are widely used. Diamond turning is often used to fabricate lenslet arrays directly or molds that are used to mold lenslet arrays. In this paper, mainly by real-time monitoring position following error for slow tool servo, different fabrication parameters are quantitatively studied and optimized for actual fabrication, then by actual fabrication validation, uniform and high-fidelity surface topography across the actual whole lenslet array is achieved. The evaluated fabrication parameters include sampling strategy, inverse time feed, arc-length, etc. The study provides a quick, effective, and detailed reference for both convex and concave lenslet array cutting parameter selection. At the end, a smooth zonal machining strategy toolpath is demonstrated for fabricating concave lenslet arrays.

 

To address color polarization demosaicking problems in polarization imaging with a color polarization camera, we propose a color polarization demosaicking convolutional neural network (CPDCNN), which has a two-branch structure to ensure the fidelity of polarization signatures and enhance image resolution. To train the network, we built a unique dual-camera system and captured a pairwise color polarization image dataset. Experimental results show that CPDCNN outperformances other methods by a large margin in contrast and resolution.

 

We present a process to locate the desired local optimum of high-dimensional design problems such as the optimization of freeform mirror systems. By encoding active design variables into a binary vector imitating DNA sequences, we are able to perform a genetic optimization of the optimization process itself. The end result is an optimization route that is effectively able to sidestep local minima by warping the variable space around them in a way that mimics the expertise of veteran designers. The generality of the approach is validated through the automated generation of high-performance designs for off-axis three- and four-mirror free-form systems.

 

Multidimensional photography can capture optical fields beyond the capability of conventional image sensors that measure only two-dimensional (2D) spatial distribution of light. By mapping a high-dimensional datacube of incident light onto a 2D image sensor, multidimensional photography resolves the scene along with other information dimensions, such as wavelength and time. However, the application of current multidimensional imagers is fundamentally restricted by their static optical architectures and measurement schemes—the mapping relation between the light datacube voxels and image sensor pixels is fixed. To overcome this limitation, we propose tunable multidimensional photography through active optical mapping. A high-resolution spatial light modulator, referred to as an active optical mapper, permutes and maps the light datacube voxels onto sensor pixels in an arbitrary and programmed manner. The resultant system can readily adapt the acquisition scheme to the scene, thereby maximising the measurement flexibility. Through active optical mapping, we demonstrate our approach in two niche implementations: hyperspectral imaging and ultrafast imaging.

 

We propose a novel and simple snapshot phase-shifting diffraction phase microscope with a polarization grating and spatial phase-shifting technology. Polarization grating separates the incident beam into left and right circular polarization beams, one of which is used as the reference beam after passing through a pinhole. Four phase-shifted interferograms can be captured simultaneously from the polarization camera to reconstruct the high spatial resolution phase map. The principle is presented in this Letter, and the performance of the proposed system is demonstrated experimentally. Due to the near-common-path configuration and snapshot feature, the proposed system provides a feasible way for real-time quantitative phase measurement with minimal sensitivity to vibration and thermal disturbance.

 

A two-frame phase-shifting interferometric wavefront reconstruction method based on deep learning is proposed. By learning from a large number of simulation data based on a physical model, the wrapped phase can be calculated accurately from two interferograms with an unknown phase step. The phase step can be any value excluding the integral multiples of π and the size of interferograms can be flexible. This method does not need a pre-filtering to subtract the direct-current term, but only needs a simple normalization. Comparing with other two-frame methods in both simulations and experiments, the proposed method can achieve better performance.

 

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.