Lensless imaging in one shot using the complex degree of coherence obtained by multiaperture interferences.

The van Cittert-Zernike theorem states that the Fourier transform of the intensity distribution function of a distant, incoherent source is equal to the complex degree of coherence. In this Letter, we present a method for measuring the complex degree of coherence in one shot by recording the interference patterns produced by multiple aperture pairs. The intensity of the sample is obtained by Fourier transforming the complex degree of coherence. The experimental verification by using a simple object is presented together with a discussion on how the method could be improved for imaging more complex samples.

Lensless microscopy by multiplane recordings: sub-micrometer, diffraction-limited, wide field-of-view imaging.

Lensless microscopy is attractive because lenses are often large, heavy and expensive. We report diffraction-limited, sub-micrometer resolution in a lensless imaging system that does not need a reference wave and imposes few restrictions on the density of the sample. We use measurements of the intensity of light scattered by the sample at multiple heights above the sample and a modified Gerchberg-Saxton algorithm to reconstruct the phase of the optical field. We introduce a pixel-splitting algorithm that increases resolution beyond the size of the sensor pixels, and implement high-dynamic-range measurements. The resolution depends on the numerical aperture of the first measurement height only, while the field of view is limited by the last measurement height only. As a result, resolution and field of view can be controlled independently. The pixel-splitting algorithm also allows imaging with light of low spatial coherence, and we show that such low coherence is beneficial for a larger field of view. Using illumination from three LEDs, we produce full-color images of biological samples. Finally, we provide a detailed analysis of the limiting factors of this lensless microscopy system. The good performance demonstrated here can allow lensless systems to replace conventional microscope objectives in some situations.

Advantages of holographic imaging through fog.

In this paper, we demonstrate digital holographic imaging through a 27-m-long fog tube filled with ultrasonically generated fog. Its high sensitivity makes holography a powerful technology for imaging through scattering media. With our large-scale experiments, we investigate the potential of holographic imaging for road traffic applications, where autonomous driving vehicles require reliable environmental perception in all weather conditions. We compare single-shot off-axis digital holography to conventional imaging (with coherent illumination) and show that holographic imaging requires 30 times less illumination power for the same imaging range. Our work includes signal-to-noise ratio considerations, a simulation model, and quantitative statements on the influence of various physical parameters on the imaging range.

Physics-enhanced neural network for phase retrieval from two diffraction patterns.

In this work, we propose a physics-enhanced two-to-one Y-neural network (two inputs and one output) for phase retrieval of complex wavefronts from two diffraction patterns. The learnable parameters of the Y-net are optimized by minimizing a hybrid loss function, which evaluates the root-mean-square error and normalized Pearson correlated coefficient on the two diffraction planes. An angular spectrum method network is designed for self-supervised training on the Y-net. Amplitudes and phases of wavefronts diffracted by a USAF-1951 resolution target, a phase grating of 200 lp/mm, and a skeletal muscle cell were retrieved using a Y-net with 100 learning iterations. Fast reconstructions could be realized without constraints or a priori knowledge of the samples.

Intrinsic parameter-free calibration of FPP using a ray phase mapping model.

This Letter presents a ray phase mapping model (RPM) for fringe projection profilometry (FPP) that avoids calibrating intrinsic parameters. The novelty of the RPM, to the best of our knowledge, is the ability to characterize the imaging system with independent rays for each pixel, and to associate the rays with the projected phase in the illumination field for efficient 3D mapping, which avoids complex imaging-specific modeling about lens layout and distortion. Two loss functions are constructed to flexibly optimize camera ray parameters and mapping coefficients, respectively. As a universal approach, it has the potential to calibrate different types of FPP systems with high accuracy. Experiments on wide-angle lens FPP, telecentric lens FPP, and micro-electromechanical system (MEMS)-based FPP are carried out to verify the feasibility of the proposed method.

Differential phase measurement based on synchronous phase shift determination.

Based on synchronous phase shift determination, we propose a differential phase measurement method for differential interference contrast (DIC) microscopy. An on-line phase shift measurement device is used to generate carrier interferograms and determine the phase shift of DIC images. Then the differential phase can be extracted with the least-squares phase-shifting algorithm. In addition to realizing on-line, dynamic, real-time, synchronous and high precision phase shift measurement, the proposed method also can reconstruct the phase of the specimen by using the phase-integral algorithm. The differential phase measurement method reveals obvious advantages in error compensation, anti-interference, and noise suppression. Both simulation analysis and experimental result demonstrate that using the proposed method, the accuracy of phase shift measurement is higher than 0.007 rad. Very accurate phase reconstructions were obtained with both polystyrene microspheres and human vascular endothelial.

Lensless phase imaging microscopy using multiple intensity diffraction patterns obtained under coherent and partially coherent illumination.

In this paper, we show how high-resolution phase imaging is obtained from multiple intensity diffraction patterns. The results of the experiments carried out with different microscopic phase and amplitude samples illuminated with coherent and partially coherent light are presented. A comparison with experimental results obtained by digital holographic microscopy is given, and advantages/disadvantages of the techniques are discussed.

Roadmap on digital holography [Invited].

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

DL-SI-DHM: a deep network generating the high-resolution phase and amplitude images from wide-field images.

Structured illumination digital holographic microscopy (SI-DHM) is a high-resolution, label-free technique enabling us to image unstained biological samples. SI-DHM has high requirements on the stability of the experimental setup and needs long exposure time. Furthermore, image synthesizing and phase correcting in the reconstruction process are both challenging tasks. We propose a deep-learning-based method called DL-SI-DHM to improve the recording, the reconstruction efficiency and the accuracy of SI-DHM and to provide high-resolution phase imaging. In the training process, high-resolution amplitude and phase images obtained by phase-shifting SI-DHM together with wide-field amplitudes are used as inputs of DL-SI-DHM. The well-trained network can reconstruct both the high-resolution amplitude and phase images from a single wide-field amplitude image. Compared with the traditional SI-DHM, this method significantly shortens the recording time and simplifies the reconstruction process and complex phase correction, and frequency synthesizing are not required anymore. By comparsion, with other learning-based reconstruction schemes, the proposed network has better response to high frequencies. The possibility of using the proposed method for the investigation of different biological samples has been experimentally verified, and the low-noise characteristics were also proved.

Phase retrieval using bidirectional interference.

In this paper we describe a phase retrieval algorithm using constraints given by diffraction patterns and phase difference obtained from bidirectional interference. Wave propagation and linear phase ramps are used to connect the recordings. At least three patterns are recorded and processed (two diffraction patterns and one interference pattern). The quality of the results can be improved when recording and processing more patterns. The method works well with non-sparse samples and short (few millimeter) recording distances. Simulations, comparisons with other methods, and experimental validations are presented.

Single-pixel scatter-plate microscopy.

Based on the optical memory effect of scattered light, we developed a new single-pixel camera concept. The retrieved images contain both 3D and spectral information about the sample. A spatial light modulator (SLM) generates a random intensity modulation. The signal recorded by the single-pixel detector is cross correlated by the calculated point spread function (PSF) signals of the SLM to retrieve the image. In this publication, both simulations and experimental results are presented.

Phase retrieval using 3D Fourier transforms of volume diffraction pattern.

In this Letter, we describe a method for retrieving the phase of a wavefield from a volume diffraction pattern. We show at first that the magnitude of the 3D Fourier transform of a diffracted volume wavefield is concentrated around a paraboloid. For the phase retrieval, we apply iteratively the constraints of the measured intensity and the paraboloid (sparsity) constraint in the 3D Fourier domain. Experimental validations and comparisons to other methods are presented.

Scatter-plate microscopy with spatially coherent illumination and temporal scatter modulation.

Scatter-plate microscopy (SPM) is a lensless imaging technique for high-resolution imaging through scattering media. So far, the method was demonstrated for spatially incoherent illumination and static scattering media. In this publication, we demonstrate that these restrictions are not necessary. We realized imaging with spatially coherent and spatially incoherent illumination. We further demonstrate that SPM is still a valid imaging method for scatter-plates, which change their scattering behaviour (i.e. the phase-shift) at each position on the plate continuously but independently from other positions. Especially we realized imaging through rotating ground glass diffusers.

Numerical dark-field imaging using deep-learning.

Dark-field microscopy is a powerful technique for enhancing the imaging resolution and contrast of small unstained samples. In this study, we report a method based on end-to-end convolutional neural network to reconstruct high-resolution dark-field images from low-resolution bright-field images. The relation between bright- and dark-field which was difficult to deduce theoretically can be obtained by training the corresponding network. The training data, namely the matched bright- and dark-field images of the same object view, are simultaneously obtained by a special designed multiplexed image system. Since the image registration work which is the key step in data preparation is not needed, the manual error can be largely avoided. After training, a high-resolution numerical dark-field image is generated from a conventional bright-field image as the input of this network. We validated the method by the resolution test target and quantitative analysis of the reconstructed numerical dark-field images of biological tissues. The experimental results show that the proposed learning-based method can realize the conversion from bright-field image to dark-field image, so that can efficiently achieve high-resolution numerical dark-field imaging. The proposed network is universal for different kinds of samples. In addition, we also verify that the proposed method has good anti-noise performance and is not affected by the unstable factors caused by experiment setup.

Lensless light-field imaging through diffuser encoding.

Microlens array-based light-field imaging has been one of the most commonly used and effective technologies to record high-dimensional optical signals for developing various potential high-performance applications in many fields. However, the use of a microlens array generally suffers from an intrinsic trade-off between the spatial and angular resolutions. In this paper, we concentrate on exploiting a diffuser to explore a novel modality for light-field imaging. We demonstrate that the diffuser can efficiently angularly couple incident light rays into a detected image without needing any lens. To characterize and analyse this phenomenon, we establish a diffuser-encoding light-field transmission model, in which four-dimensional light fields are mapped into two-dimensional images via a transmission matrix describing the light propagation through the diffuser. Correspondingly, a calibration strategy is designed to flexibly determine the transmission matrix, so that light rays can be computationally decoupled from a detected image with adjustable spatio-angular resolutions, which are unshackled from the resolution limitation of the sensor. The proof-of-concept approach indicates the possibility of using scattering media for lensless four-dimensional light-field recording and processing, not just for two- or three-dimensional imaging.

Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning.

In this manuscript, we propose a quantitative phase imaging method based on deep learning, using a single wavelength illumination to realize dual-wavelength phase-shifting phase recovery. By using the conditional generative adversarial network (CGAN), from one interferogram recorded at a single wavelength, we obtain interferograms at other wavelengths, the corresponding wrapped phases and then the phases at synthetic wavelengths. The feasibility of the proposed method is verified by simulation and experiments. The results demonstrate that the measurement range of single-wavelength interferometry (SWI) is improved by keeping a simple setup, avoiding the difficulty caused by using two wavelengths simultaneously. This will provide an effective solution for the problem of phase unwrapping and the measurement range limitation in phase-shifting interferometry.

Single-shot structured-light-field three-dimensional imaging.

This Letter reports an approach to single-shot three-dimensional (3D) imaging that is combining structured illumination and light-field imaging. The sinusoidal distribution of the radiance in the structured-light field can be processed and transformed to compute the angular variance of the local radiance difference. The angular variance across the depth range exhibits a single-peak distribution trend that can be used to obtain the unambiguous depth. The phase computation that generally requires the acquisition of multi-frame phase-shifting images is no longer mandatory, thus enabling single-shot structured-light-field 3D imaging. The proposed approach was experimentally demonstrated through a dynamic scene.

Phase imaging with an untrained neural network.

Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand, thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design, in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

Light-field depth estimation considering plenoptic imaging distortion.

Light-field imaging can simultaneously record spatio-angular information of light rays to carry out depth estimation via depth cues which reflect a coupling of the angular information and the scene depth. However, the unavoidable imaging distortion in a light-field imaging system has a side effect on the spatio-angular coordinate computation, leading to incorrectly estimated depth maps. Based on the previously established unfocused plenoptic metric model, this paper reports a study on the effect of the plenoptic imaging distortion on the light-field depth estimation. A method of light-field depth estimation considering the plenoptic imaging distortion is proposed. Besides, the accuracy analysis of the light-field depth estimation was performed by using standard components. Experimental results demonstrate that efficiently compensating the plenoptic imaging distortion results in a six-fold improvement in measuring accuracy and more consistency across the measuring depth range. Consequently, the proposed method is proved to be suitable for light-field depth estimation and three-dimensional measurement with high quality, enabling unfocused plenoptic cameras to be metrological tools in the potential application scenarios such as industry, biomedicine, entertainment, and many others.

Spectral Object Recognition in Hyperspectral Holography with Complex-Domain Denoising.

In this paper, we have applied a recently developed complex-domain hyperspectral denoiser for the object recognition task, which is performed by the correlation analysis of investigated objects' spectra with the fingerprint spectra from the same object. Extensive experiments carried out on noisy data from digital hyperspectral holography demonstrate a significant enhancement of the recognition accuracy of signals masked by noise, when the advanced noise suppression is applied.