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Imaging with undetected photons relies upon nonlinear interferometry to extract the spatial image from an infrared probe beam and reveal it in the interference pattern of an easier-to-detect visible beam. Typically, the transmission and phase images are extracted using phase-shifting techniques and combining interferograms from multiple frames. Here we show that off-axis digital holography enables reconstruction of both transmission and phase images at the infrared wavelength from a single interferogram, and hence a single frame, recorded in the visible. This eliminates the need for phase stepping and multiple acquisitions, thereby greatly reducing total measurement time for imaging with long acquisition times at low flux or enabling video-rate imaging at higher flux. With this single-frame acquisition technique, we are able to reconstruct transmission images of an object in the infrared beam with a signal-to-noise ratio of $1.78\,\pm\,0.06$ at 10 frames per second, and record a dynamic scene in the infrared beam at 33 frames per second., Comment: 8 pages, 5 figures, references added to page 1

Deep learning-based applications have seen a lot of success in recent years. Text, audio, image, and video have all been explored with great success using deep learning approaches. The use of convolutional neural networks (CNN) in computer vision, in particular, has yielded reliable results. In order to achieve these results, a large amount of data is required. However, the dataset cannot always be accessible. Moreover, annotating data can be difficult and time-consuming. Self-training is a semi-supervised approach that managed to alleviate this problem and achieve state-of-the-art performances. Theoretical analysis even proved that it may result in a better generalization than a normal classifier. Another problem neural networks can face is the increasing complexity of modern problems, requiring a high computational and storage cost. One way to mitigate this issue, a strategy that has been inspired by human cognition known as modular learning, can be employed. The principle of the approach is to decompose a complex problem into simpler sub-tasks. This approach has several advantages, including faster learning, better generalization, and enables interpretability. In the first part of this paper, we introduce and evaluate different architectures of modular learning for Dorsal Capsulo-Scapholunate Septum (DCSS) instability classification. Our experiments have shown that modular learning improves performances compared to non-modular systems. Moreover, we found that weighted modular, that is to weight the output using the probabilities from the gating module, achieved an almost perfect classification. In the second part, we present our approach for data labeling and segmentation with self-training applied on shoulder arthroscopy images.

We present a novel solver technique for the anisotropic heat flux equation, aimed at the high level of anisotropy seen in magnetic confinement fusion plasmas. Such problems pose two major challenges: (i) discretization accuracy and (ii) efficient implicit linear solvers. We simultaneously address each of these challenges by constructing a new finite element discretization with excellent accuracy properties, tailored to a novel solver approach based on algebraic multigrid (AMG) methods designed for advective operators. We pose the problem in a mixed formulation, introducing the directional temperature gradient as an auxiliary variable. The temperature and auxiliary fields are discretized in a scalar discontinuous Galerkin space with upwinding principles used for discretizations of advection. We demonstrate the proposed discretization's superior accuracy over other discretizations of anisotropic heat flux, achieving error $1000\times$ smaller for anisotropy ratio of $10^9$, for $closed$ $field$ $lines$. The block matrix system is reordered and solved in an approach where the two advection operators are inverted using AMG solvers based on approximate ideal restriction (AIR), which is particularly efficient for upwind discontinuous Galerkin discretizations of advection. To ensure that the advection operators are non-singular, in this paper we restrict ourselves to considering open (acyclic) magnetic field lines for the linear solvers. We demonstrate fast convergence of the proposed iterative solver in highly anisotropic regimes where other diffusion-based AMG methods fail., Comment: 29 pages, 5 figures

We present a wide-field homodyne imaging system capable of recovering intensity and phase images of an object from a single camera frame at an illumination intensity significantly below the noise floor of the camera. By interfering a weak imaging signal with a much brighter reference beam we are able to image objects in the short-wave infrared down to signal intensity of $\sim$$1.1$ photons per pixel per frame incident on the sensor despite the camera having a noise floor of $\sim$$200$ photons per pixel. At this illumination level we operate under the conditions of a reference beam to probe beam power ratio of $\sim$$300$,$000$:$1$. There is a corresponding $29.2\%$ drop in resolution of the image due to the method implemented. For transmissive objects, in addition to intensity, the approach also images the phase profile of the object. We believe our demonstration could open the way to low-light imaging in domains where low noise cameras are not available, thus vastly extending the range of application for low-light imaging.

Spontaneous downconversion is a versatile source for correlated biphotons that has been employed in many quantum sensing and imaging experiments. Spatially-resolved photon-counting detectors allow to access a large number of modes, posing the challenge of an accurate description of such systems. We propose a simple model to generate images as though acquired by a photon-counting camera, and allow to simulate and characterise the detection of quantum correlations. We derive quantitative parameters characteristic of the spatial correlations for a given experiment, comparing the images produced by our model to the frames acquired by an electron-multiplying CCD camera. Moreover we accurately predict the decreased detection of spatially correlated biphotons caused by introducing a variable amount of optical loss after the nonlinear crystal, even though the total number of detected events is kept constant, showing excellent agreement between model and experiment.

Light-field cameras allow the acquisition of both the spatial and angular components of the light. This has a wide range of applications from image refocusing to 3D reconstruction of a scene. The conventional way to perform such acquisitions leads to a strong spatio-angular resolution limit. Here we propose a computational version of the light-field camera. We perform a one gigapixel photo-realistic diffraction limited light-field acquisition, that would require the use of a one gigapixel sensor were the acquisition to be performed with a conventional light-field camera. This result is mostly limited by the total acquisition time, as our system could in principle allow $\sim$Terapixel reconstructions to be achieved. The reported result presents many potential advantages, such as the possibility to perform large depth of field light-field acquisitions, realistic refocusing along a very wide range of depths, very high dimensional super-resolved image acquisitions, and large depth of field 3D reconstructions.

The production of pairs of entangled photons simply by focusing a laser beam onto a crystal with a non-linear optical response was used to test quantum mechanics and to open new approaches in imaging. The development of the latter was enabled by the emergence of single photon sensitive cameras able to characterize spatial correlations and high-dimensional entanglement. Thereby new techniques emerged such as the ghost imaging of objects - where the quantum correlations between photons reveal the image from photons that have never interacted with the object - or the imaging with undetected photons by using nonlinear interferometers. Additionally, quantum approaches in imaging can also lead to an improvement in the performance of conventional imaging systems. These improvements can be obtained by means of image contrast, resolution enhancement that exceed the classical limit and acquisition of sub-shot noise phase or amplitude images. In this review we discuss the application of quantum states of light for advanced imaging techniques.

The contrast of an image can be degraded by the presence of background light and sensor noise. To overcome this degradation, quantum illumination protocols have been theorised (Science 321 (2008), Physics Review Letters 101 (2008)) that exploit the spatial correlations between photon-pairs. Here we demonstrate the first full-field imaging system using quantum illumination, by an enhanced detection protocol. With our current technology we achieve a rejection of background and stray light of order 5 and also report an image contrast improvement up to a factor of 5.5, which is resilient to both environmental noise and transmission losses. The quantum illumination protocol differs from usual quantum schemes in that the advantage is maintained even in the presence of noise and loss. Our approach may enable laboratory-based quantum imaging to be applied to real-world applications where the suppression of background light and noise is important, such as imaging under low-photon flux and quantum LIDAR.

We consider finite-dimensional irreducible transitive graded Lie algebras $L = \sum_{i=-q}^rL_i$ over algebraically closed fields of characteristic three. We assume that the null component $L_0$ is classical and reductive. The adjoint representation of $L$ on itself induces a representation of the commutator subalgebra $L_0'$ of the null component on the minus-one component $L_{-1}.$ We show that if the depth $q$ of $L$ is greater than one, then this representation must be restricted., Comment: 46 pages; correct typos; change argument; one hypothesis of result changed: height of Lie algebra now required to be at least two