Noninvasive holographic imaging through dynamically scattering media.

We present a noninvasive method for quantitative phase imaging through dynamically scattering media. A complex amplitude object, illuminated with coherent light, is captured through a dynamically scattering medium and a variable coded aperture, without the need for interferometric measurements or imaging optics. The complex amplitude of the object is computationally retrieved from intensity images that use multiple coded aperture patterns, employing a stochastic gradient descent algorithm. We demonstrate the proposed method both numerically and experimentally.

Extrapolated speckle-correlation imaging with an untrained deep neural network.

We present a method for speckle-correlation imaging with an extended field of view to observe spatially non-sparse objects. In speckle-correlation imaging, an object is recovered from a non-invasively captured image through a scattering medium by assuming shift-invariance of the optical process called the memory effect. The field of view of speckle-correlation imaging is limited by the size of the memory effect, and it can be extended by extrapolating the speckle correlation in the reconstruction process. However, spatially sparse objects are assumed in the inversion process because of its severe ill-posedness. To address this issue, we introduce a deep image prior, which regularizes the image statistics by using the structure of an untrained convolutional neural network, to speckle-correlation imaging. We experimentally demonstrated the proposed method and showed the possibility of extending the method to imaging through scattering media.

Low-Rank Combinatorial Optimization and Statistical Learning by Spatial Photonic Ising Machine.

The spatial photonic Ising machine (SPIM) [13D. Pierangeli et al., Large-Scale Photonic Ising Machine by Spatial Light Modulation, Phys. Rev. Lett. 122, 213902 (2019).PRLTAO0031-900710.1103/PhysRevLett.122.213902] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.

Spatial-photonic Ising machine by space-division multiplexing with physically tunable coefficients of a multi-component model.

This paper proposes a space-division multiplexed spatial-photonic Ising machine (SDM-SPIM) that physically calculates the weighted sum of the Ising Hamiltonians for individual components in a multi-component model. Space-division multiplexing enables tuning a set of weight coefficients as an optical parameter and obtaining the desired Ising Hamiltonian at a time. We solved knapsack problems to verify the system's validity, demonstrating that optical parameters impact the search property. We also investigated a new dynamic coefficient search algorithm to enhance search performance. The SDM-SPIM would physically calculate the Hamiltonian and a part of the optimization with an electronics process.

Single-shot blind deconvolution with coded aperture.

In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA). In this method, we utilize the CA, inserted on the pupil plane, as support constraints in blind deconvolution. Not only an object is estimated, but also a point spread function of turbulence from a single captured image by a reconstruction algorithm with CA support. The proposed method is demonstrated by simulation and an experiment in which point sources are recovered under severe turbulence.

Incoherent optical phase conjugation.

Optical phase conjugation is a known technique for optically reproducing an object behind a scattering medium. Here we present digital optical phase conjugation through scattering media with spatially and temporally incoherent light. This enables us to eliminate the inevitable light coherence and the need for interferometric measurement for optical phase conjugation. Moreover, we show a method for suppressing background noise, which is critical in incoherent optical phase conjugation. We numerically and experimentally demonstrate the proposed method with background suppression.

Quantitative analysis of nonlinear optical input/output of a quantum-dot network based on the echo state property.

The echo state property, which is related to the dynamics of a neural network excited by input driving signals, is one of the well-known fundamental properties of recurrent neural networks. During the echo state, the neural network reveals an internal memory function that enables it to remember past inputs. Due to the echo state property, the neural network will asymptotically update its condition from the initial condition and is expected to exhibit temporally nonlinear input/output. As a physical neural network, we fabricated a quantum-dot network that is driven by sequential optical-pulse inputs and reveals corresponding outputs, by random dispersion of quantum-dots as its components. In the network, the localized optical energy of excited quantum-dots is allowed to transfer to neighboring quantum-dots, and its stagnation time due to multi-step transfers corresponds to the hold time of the echo state of the network. From the experimental results of photon counting of the fluorescence outputs, we observed nonlinear optical input/output of the quantum-dot network due to its echo state property. Its nonlinearity was quantitatively verified by a correlation analysis. As a result, the relation between the nonlinear input/outputs and the individual compositions of the quantum-dot network was clarified.

Digital-optical computational imaging capable of end-point logic operations.

In this study, digital-optical computational imaging is proposed for object data transmission with a capability to achieve end-point logic operations over free-space data transmission. The framework is regarded as an extension of computational imaging using digital-optical codes originally developed for digital optical computing. Spatial code patterns for optical logic operations are extended to digital-optical codes in the temporal and spectral domains. The physical form of the digital-optical codes is selected, as appropriate, for the situation in use, and different forms can be combined to increase the data-transmission bandwidth. The encoded signals are transferred over free space and decoded by a simple procedure on the destination device, thus enabling logic operations at the end-point of the data transmission. To utilize the benefits of digital processing, a data-transfer mode is introduced which assigns preprocessing for the signals to be encoded and the end-point processing. As a demonstration of the proposed method, an experimental testbed was constructed assuming data transmission from sensor nodes to a gateway device appearing in the Internet of Things. In the experiment, encrypted signals of the sensor nodes, which were encoded by spatial digital-optical codes on RGB channels, were captured as an image, and the original signals were retrieved correctly by an end-point exclusive OR operation.

Compressive propagation with coherence.

In this Letter, we present wave propagation models of spatially partially coherent (or spatially incoherent) light to compress the computational load of forward and back propagations in inverse problems. In our model, partially coherent light is approximated as a set of random or plane wavefronts passing through spatial bandpass filters, which corresponds to an illumination pupil, and each wave coherently propagates onto a sensor plane through object space. We show that our models reduce the number of coherent propagations in inverse problems, which are essential in optical control and sensing, such as computer-generated holography (CGH) and quantitative phase imaging. We verify the proposed models by numerical and experimental demonstrations of CGH incorporating spatially partially coherent light.

Spatial frequency domain imager based on a compact multiaperture camera: testing and feasibility for noninvasive burn severity assessment.

SIGNIFICANCE: Spatial frequency domain imaging (SFDI) is a wide-field imaging technique that provides quantitative maps of tissue optical properties. We describe a compact SFDI imager that employs a multispectral compound-eye camera. This design enables simultaneous image acquisition at multiple wavelengths. Such a device has potential for application for quantitative evaluation of superficial tissues by nonspecialists in low-resource settings. AIM: The aim of this work was to develop a compact SFDI imager for widefield imaging of in-vivo tissue optical properties and verify its ability to measure optical properties of tissue-simulating phantoms and in a preclinical model of burn wounds. APPROACH: This compound-eye imager was constructed using a CMOS sensor subdivided into multiple regions, each having a bandpass filter and objective lens. The ability of the instrument to image optical properties was compared with (1) a commercial SFDI imager and (2) a laboratory-based system. Initial validation of ability to accurately characterize optical properties was performed using a tissue-simulating optical phantom. It was then applied to an established murine model of thermal contact burn severity. In-vivo measurements of the optical properties of rat skin were performed before and after the application of burns. Histology was used to verify burn severity. RESULTS: Measurements of the tissue-simulating phantom optical properties made using the compound-eye imager agree with measurements made using the two comparison SFDI devices. For the murine burn model, the burns showed a decrease in the reduced scattering coefficient at all measurement wavelengths compared with preburn measurements at the same locations. This is consistent with previously reported changes in scattering that occur in full-thickness burns. CONCLUSION: We demonstrate the potential for SFDI to be translated into compact form factor using a compound-eye camera that is capable of obtaining multiple wavelengths channels simultaneously.

Unconventional Imaging: introduction to the feature issue.

This feature issue of Applied Optics is dedicated to the international meeting of Information Photonics 2020 (IP'20), which was held September 11-12, 2020, in Taipei, Taiwan. IP'20 covered a broad range of topics, including advanced display techniques, optical computing, and optical storage. This feature issue, however, limits topics to unconventional imaging techniques, such as digital holography, artificial-intelligence associated imaging, compressive imaging, and single-pixel imaging.

Spectral speckle-correlation imaging.

We present a method for single-shot spectrally resolved imaging through scattering media by using the spectral memory effect of speckles. In our method, a single speckle pattern from a multi-colored object is captured through scattering media with a monochrome image sensor. The color object is recovered by correlation of the captured speckle and a three-dimensional phase retrieval process. The proposed method was experimentally demonstrated by using point sources with different emission spectra located between diffusers. This study paves the way for non-invasive and low-cost spectral imaging through scattering media.

Three-dimensional deeply generated holography [Invited].

In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.

Imaging through scattering media based on semi-supervised learning.

We present a method for less-invasive imaging through scattering media. We use an image-to-image translation, which is called a cycle generative adversarial network (CycleGAN), based on semi-supervised learning with an unlabeled dataset. Our method was experimentally demonstrated by reconstructing object images displayed on a spatial light modulator between diffusers. In the demonstration, CycleGAN was trained with captured images and object candidate images that were not used for image capturing through the diffusers and were not paired with the captured images.

Deeply coded aperture for lensless imaging.

In this Letter, we present a method for jointly designing a coded aperture and a convolutional neural network for reconstructing an object from a single-shot lensless measurement. The coded aperture and the reconstruction network are connected with a deep learning framework in which the coded aperture is placed as a first convolutional layer. Our co-optimization method was experimentally demonstrated with a fully convolutional network, and its performance was compared to a coded aperture with a modified uniformly redundant array.

Optical decomposition of DNA gel and modification of object mobility on micrometre scale.

DNA gels can be engineered to exhibit specific properties through the choice of DNA sequences and modification with dye molecules, and can therefore be useful in biomedical applications such as the detection of biomolecules. State transitions of DNA gels on the micrometre scale can generate a viscosity gradient, which can be used to modify the mobility of micrometre-sized objects. In this paper, we propose a method for changing the viscosity of DNA gels using optical decomposition. The use of light allows for decomposition on the micrometre scale, which can be used to achieve patterned viscosity changes within DNA gels. Decomposition was induced by thermal energy released through non-radiative relaxation of excited quenchers. We demonstrated the decomposition of DNA gels in response to irradiation patterns on the micrometre scale. In addition, as a result of changes in DNA gel viscosity due to decomposition, the mobility of polystyrene beads was shown to increase. This technique could provide a new optical approach for controlling the mobility of micrometre-sized objects.

Single-shot noninvasive three-dimensional imaging through scattering media.

We present a method for single-shot three-dimensional imaging through scattering media with a three-dimensional memory effect. In the proposed computational process, a captured speckle image is two-dimensionally correlated with different scales, and the object is three-dimensionally recovered with three-dimensional phase retrieval. Our method was experimentally demonstrated with a lensless setup and was compared with a multishot approach used in our previous work [Opt. Lett.44, 2526 (2019)OPLEDP0146-959210.1364/OL.44.002526].

Noninvasive three-dimensional imaging through scattering media by three-dimensional speckle correlation.

We present a method for noninvasive three-dimensional imaging through scattering media by using a three-dimensional memory effect in scattering phenomena. In the proposed method, an object in a scattering medium is reconstructed from a three-dimensional autocorrelation of speckle images captured by axially scanning an image sensor, based on a three-dimensional phase retrieval algorithm. We experimentally demonstrated our method with a lensless setup by using a three-dimensionally printed object between diffusers.

Diffusion-based single-shot diffraction tomography.

Holographic microscopy is a powerful technique for noninvasive label-free biomedical imaging. Most holographic methods utilize reference light and/or multiple measurements to observe both the amplitude and phase of a light wave passing through a specimen. However, such fundamental requirements degrade the spatial resolution due to the use of a reference carrier, cause difficulties for real-time imaging of dynamic biological events, and make the optical setups bulky. Here, we realized reference-free, single-shot holographic tomography by just inserting a diffuser into the optical path in a conventional microscope setup to generate randomly structured illumination. A three-dimensional complex amplitude field was reconstructed from a single scattered intensity image by means of sparsity-constrained multislice phase retrieval.

Deep learning wavefront sensing.

We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources.