Accelerating quad Airy beams-based point response for interferenceless coded aperture correlation holography.

Interferenceless-coded aperture correlation holography (I-COACH) is a promising single-shot 3D imaging method in which a coded phase mask (CPM) is used to encode 3D information about an object into an intensity distribution. However, conventional CPM encoding methods usually lead to intensity dilution, especially in the recording of point spread holograms (PSHs), resulting in low-resolution reconstruction of I-COACH. Here, we propose accelerating quad Airy beams with four mainlobes as a point response to enable weak diffraction propagation and a sharp maximum intensity in the transverse direction. Moreover, the four mainlobes exhibit lateral acceleration in 3D space, so the PSHs in different axial positions show a unique and concentrated intensity distribution on the image sensor, thereby realizing a high-resolution reconstruction of I-COACH. Compared with conventional CPM encoding methods, the proposed accelerating quad Airy-beam-encoding method has superior performance in improving the resolution of I-COACH reconstruction even in the presence of external interference.

Single-shot Fresnel incoherent correlation holography via deep learning based phase-shifting technology.

Fresnel incoherent correlation holography (FINCH) realizes non-scanning three-dimension (3D) images using spatial incoherent illumination, but it requires phase-shifting technology to remove the disturbance of the DC term and twin term that appears in the reconstruction field, thus increasing the complexity of the experiment and limits the real-time performance of FINCH. Here, we propose a single-shot Fresnel incoherent correlation holography via deep learning based phase-shifting (FINCH/DLPS) method to realize rapid and high-precision image reconstruction using only a collected interferogram. A phase-shifting network is designed to implement the phase-shifting operation of FINCH. The trained network can conveniently predict two interferograms with the phase shift of 2/3 π and 4/3 π from one input interferogram. Using the conventional three-step phase-shifting algorithm, we can conveniently remove the DC term and twin term of the FINCH reconstruction and obtain high-precision reconstruction through the back propagation algorithm. The Mixed National Institute of Standards and Technology (MNIST) dataset is used to verify the feasibility of the proposed method through experiments. In the test with the MNIST dataset, the reconstruction results demonstrate that in addition to high-precision reconstruction, the proposed FINCH/DLPS method also can effectively retain the 3D information by calibrating the back propagation distance in the case of reducing the complexity of the experiment, further indicating the feasibility and superiority of the proposed FINCH/DLPS method.

Single-shot image restoration via a model-enhanced network with unpaired supervision in an optical sparse aperture system.

We propose a model-enhanced network with unpaired single-shot data for solving the imaging blur problem of an optical sparse aperture (OSA) system. With only one degraded image captured from the system and one "arbitrarily" selected unpaired clear image, the cascaded neural network is iteratively trained for denoising and restoration. With the computational image degradation model enhancement, our method is able to improve contrast, restore blur, and suppress noise of degraded images in simulation and experiment. It can achieve better restoration performance with fewer priors than other algorithms. The easy selectivity of unpaired clear images and the non-strict requirement of a custom kernel make it suitable and applicable for single-shot image restoration of any OSA system.

Real-time phase imaging with physics-enhanced network and equivariance.

Learning-based phase imaging balances high fidelity and speed. However, supervised training requires unmistakable and large-scale datasets, which are often hard or impossible to obtain. Here, we propose an architecture for real-time phase imaging based on physics-enhanced network and equivariance (PEPI). The measurement consistency and equivariant consistency of physical diffraction images are used to optimize the network parameters and invert the process from a single diffraction pattern. In addition, we propose a regularization method based total variation kernel (TV-K) function constraint to output more texture details and high-frequency information. The results show that PEPI can produce the object phase quickly and accurately, and the proposed learning strategy performs closely to the fully supervised method in the evaluation function. Moreover, the PEPI solution can handle high-frequency details better than the fully supervised method. The reconstruction results validate the robustness and generalization ability of the proposed method. Specially, our results show that PEPI leads to considerable performance improvement on the imaging inverse problem, thereby paving the way for high-precision unsupervised phase imaging.

Simultaneous measurement of near-water-film air temperature and humidity fields based on dual-wavelength digital holographic interferometry.

We present a method to reconstruct the near-water-film air temperature and humidity distributions synchronously by measuring the phase delays based on dual-wavelength digital holographic interferometry. A falling water film device was used to create a water film evaporation environment and generate axially uniform temperature and humidity fields. The relationship between air temperature, humidity and phase delay is derived from the Edlen equations. With such relationship, the temperature and humidity distributions can be solved directly according to phase delays of two different wavelengths. An edge phase enhancement method and an error elimination method with PSO are presented to improve the measurement accuracy. The temperature and humidity fields in the falling water film model were experimentally reconstructed with temperature deviation of 0.06% and relative humidity deviation of 2.61%.

Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement.

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.

Registration of multi-modal images under a complex background combining multiscale features extraction and semantic segmentation.

Multi-modal imaging technology has a very broad application value in target recognition and other fields, and image registration is one of its key technologies. In this paper, a multi-modal image registration algorithm that combines multiscale features extraction and semantic segmentation is proposed to achieve accurate registration of polarized images and near-infrared images under complex backgrounds. A classical convolutional neural network ResNet is employed to capture the robust feature descriptors, and a convolutional neural network with an attention mechanism is trained to filter out the irrelevant feature points. Further, the two multi-modal images can be further registered. The experimental results show the feasibility and effectiveness of the proposed method.

Light-field focusing and modulation through scattering media based on dual-polarization-encoded digital optical phase conjugation.

Digital optical phase conjugation (DOPC) can be applied for light-field focusing and imaging through or within scattering media. Traditional DOPC only recovers the phase but loses the polarization information of the original incident beam. In this Letter, we propose a dual-polarization-encoded DOPC to recover the full information (both phase and polarization) of the incident beam. The phase distributions of two orthogonal polarization components of the speckle field coming from a multimode fiber are first measured by using digital holography. Then, the phase distributions are separately modulated on two beams and their conjugations are superposed to recover the incident beam through the fiber. By changing the phase difference or amplitude ratio between the two conjugate beams, light fields with complex polarization distribution can also be generated. This method will broaden the application scope of DOPC in imaging through scattering media.

Dual-wavelength surface plasmon resonance holographic microscopy for simultaneous measurements of cell-substrate distance and cytoplasm refractive index.

Studying the basic characteristics of living cells is of great significance in biological research. Bio-physical parameters, including cell-substrate distance and cytoplasm refractive index (RI), can be used to reveal cellular properties. In this Letter, we propose a dual-wavelength surface plasmon resonance holographic microscopy (SPRHM) to simultaneously measure the cell-substrate distance and cytoplasm RI of live cells in a wide-field and non-intrusive manner. Phase-contrast surface plasmon resonance (SPR) images of individual cells at wavelengths of 632.8 nm and 690 nm are obtained using an optical system. The two-dimensional distributions of cell-substrate distance and cytoplasm RI are then demodulated from the phase-contrast SPR images of the cells. MDA-MB-231 cells and IDG-SW3 cells are experimentally measured to verify the feasibility of this approach. Our method provides a useful tool in biological fields for dual-parameter detection and characterization of live cells.

PredictionNet: a long short-term memory-based attention network for atmospheric turbulence prediction in adaptive optics.

Adaptive optics (AO) has great applications in many fields and has attracted wide attention from researchers. However, both traditional and deep learning-based AO methods have inherent time delay caused by wavefront sensors and controllers, leading to the inability to truly achieve real-time atmospheric turbulence correction. Hence, future turbulent wavefront prediction plays a particularly important role in AO. Facing the challenge of accurately predicting stochastic turbulence, we combine the convolutional neural network with a turbulence correction time series model and propose a long short-term memory attention-based network, named PredictionNet, to achieve real-time AO correction. Especially, PredictionNet takes the spatiotemporal coupling characteristics of turbulence wavefront into consideration and can improve the accuracy of prediction effectively. The combination of the numerical simulation by a professional software package and the real turbulence experiment by digital holography demonstrates in detail that PredictionNet is more accurate and more stable than traditional methods. Furthermore, the result compared with AO without prediction confirms that predictive AO with PredictionNet is useful.

Dual-channel illumination surface plasmon resonance holographic microscopy for resolution improvement.

Surface plasmon resonance holographic microscopy (SPRHM), combining digital holographic microscopy with surface plasmon resonance (SPR), can simultaneously obtain the amplitude and phase distributions of the reflected beam carrying specimen information in SPR. Due to the decaying length of the surface plasmon wave as large as tens of micrometers, the spatial resolution of SPRHM is lower than that of ordinary optical microscopes. In this work, we propose a scheme to improve the spatial resolution of SPRHM by applying dual-channel SPR excitations. Through the polarization multiplexing technique, two holograms carrying the information of SPR excited in orthogonal directions are simultaneously acquired. Via a numerical reconstruction and filtering algorithm for holograms, the lateral spatial resolution of SPRHM can be effectively enhanced to reach nearly 1 µm at a wavelength of 632.8 nm. This is comparable to the resolution of traditional optical microscopes, while possessing the advantages of wide-field imaging and high measurement sensitivity of SPR.

Sparse-view imaging of a fiber internal structure in holographic diffraction tomography via a convolutional neural network.

Deep learning has recently shown great potential in computational imaging. Here, we propose a deep-learning-based reconstruction method to realize the sparse-view imaging of a fiber internal structure in holographic diffraction tomography. By taking the sparse-view sinogram as the input and the cross-section image obtained by the dense-view sinogram as the ground truth, the neural network can reconstruct the cross-section image from the sparse-view sinogram. It performs better than the corresponding filtered back-projection algorithm with a sparse-view sinogram, both in the case of simulated data and real experimental data.

Analysis of the effects of magnetic levitation to simulate microgravity environment on the Arp2/3 complex pathway in macrophage.

With dwindling natural resources on earth, current and future generations will need to explore space to new planets that will require travel under no or varying gravity conditions. Hence, long-term space missions and anticipated impacts on human biology such as changes in immune function are of growing research interest. Here, we reported new findings on mechanisms of immune response to microgravity with a focus on macrophage as a cellular model. We employed a superconducting magnet to generate a simulated microgravity environment and evaluated the effects of simulated microgravity on RAW 264.7 mouse macrophage cell line in three time frames: 8, 24, and 48 h. As study endpoints, we measured cell viability, phagocytosis, and used next-generation sequencing to explore its changing mechanism. Macrophage cell viability and phagocytosis both showed a marked decrease under microgravity. The differentially expressed genes (DEG) were identified in two ways: (1) gravity-dependent DEG, compared µg samples and 1 g samples at each time point; (2) time-dependent DEG, compared time-point samples within the same gravitational field. Through transcriptome analysis and confirmed by molecular biological verification, our findings firstly suggest that microgravity might affect macrophage phagocytosis by targeting Arp2/3 complex involved cytoskeleton synthesis and causing macrophage immune dysfunction. Our findings contribute to an emerging body of scholarship on biological effects of space travel.

Classification of cell morphology with quantitative phase microscopy and machine learning.

We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.

Y4-Net: a deep learning solution to one-shot dual-wavelength digital holographic reconstruction.

In this Letter, a deep learning solution (Y4-Net, four output channels network) to one-shot dual-wavelength digital holography is proposed to simultaneously reconstruct the complex amplitude information of both wavelengths from a single digital hologram with high efficiency. In the meantime, by using single-wavelength results as network ground truth to train the Y4-Net, the challenging spectral overlapping problem in common-path situations is solved with high accuracy.

Structured light beams created through a multimode fiber via virtual Fourier filtering based on digital optical phase conjugation.

Digital optical phase conjugation (DOPC) is a newly developed technique in wavefront shaping to control light propagation through complex media. Currently, DOPC has been demonstrated for the reconstruction of two- and three-dimensional targets and enabled important applications in many areas. Nevertheless, the reconstruction results are only phase conjugated to the original input targets. Herein, we demonstrate that DOPC could be further developed for creating structured light beams through a multimode fiber (MMF). By applying annular filtering in the virtual Fourier domain of the acquired speckle field, we realize the creation of the quasi-Bessel and donut beams through the MMF. In principle, arbitrary amplitude and/or phase circular symmetry filtering could be performed in the Fourier domain, thus generating the corresponding point spread functions. We expect that the reported technique can be useful for super-resolution endoscopic imaging and optical manipulation through MMFs.

Measurement of full polarization states with hybrid holography based on geometric phase.

We propose a method for measuring the full polarization states of a light field by using hybrid polarization-angular multiplexing digital holography based on geometric phase. Through acquiring the geometric phase distribution of the whole light field by only recording a composite hologram, and according to quantitative relationship between the geometric phase and polarization state, the Stokes parameters of a light field can be calculated. Compared with other methods, this method can be used to obtain the complex amplitude information of the light field simultaneously without requiring other complex devices or elements to be adjusted, thus enabling dynamic polarization state measurement. The measurement results of the light fields generated by standard polarized optical elements, vortex half-wave retarder, and liquid crystal depolarizer verified this method's feasibility and validity.

One-step robust deep learning phase unwrapping.

Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing (undersampling) are present. We propose a database generation method for phase-type objects and a one-step deep learning phase unwrapping method. With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network. Excellent anti-noise and anti-aliasing performances outperforming classical methods are highlighted in this paper.

Measurement of thermal effect in laser pumped silicon employing infrared digital holographic interferometry.

We present a short-coherence infrared digital holographic interferometry (IRDHI) to quantitatively measure the weak thermal effect in silicon wafer under visible laser pumping. In IRDHI, a superluminescent diode and a narrow-band filter are introduced to eliminate the self-interference fringes and suppress the noise. The effect of coherence length of the detection light source is analyzed and the optimal coherence length range in the proposed configuration is given. Meanwhile, we measure the weak thermal effect in silicon pumped by two different approaches of a continuous visible laser with different powers. The proposed configuration, which shows high stability and sensitivity, can be easily adapted and improved to measure the variation of thermal effect or refractive index in other near infrared transparent materials.

Y-Net: a one-to-two deep learning framework for digital holographic reconstruction.

In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.