Diffraction-Limited Plenoptic Imaging with Correlated Light

Quantum imaging of biological organisms through spatial and polarization entanglement

Quantum imaging holds potential benefits over classical imaging but has faced challenges such as poor signal-to-noise ratios, low resolvable pixel counts, difficulty in imaging biological organisms, and inability to quantify full birefringence properties. Here, we introduce quantum imaging by coincidence from entanglement (ICE), using spatially and polarization-entangled photon pairs to overcome these challenges. With spatial entanglement, ICE offers higher signal-to-noise ratios, greater resolvable pixel counts, and the ability to image biological organisms. With polarization entanglement, ICE provides quantitative quantum birefringence imaging capability, where both the phase retardation and the principal refractive index axis angle of an object can be remotely and instantly quantified without changing the polarization states of the photons incident on the object. Furthermore, ICE enables 25 times greater suppression of stray light than classical imaging. ICE has the potential to pave the way for quantum imaging in diverse fields, such as life sciences and remote sensing.

Deep learning approach for denoising low-SNR correlation plenoptic images

Correlation Plenoptic Imaging (CPI) is a novel volumetric imaging technique that uses two sensors and the spatio-temporal correlations of light to detect both the spatial distribution and the direction of light. This novel approach to plenoptic imaging enables refocusing and 3D imaging with significant enhancement of both resolution and depth of field. However, CPI is generally slower than conventional approaches due to the need to acquire sufficient statistics for measuring correlations with an acceptable signal-to-noise ratio (SNR). We address this issue by implementing a Deep Learning application to improve image quality with undersampled frame statistics. We employ a set of experimental images reconstructed by a standard CPI architecture, at three different sampling ratios, and use it to feed a CNN model pre-trained through the transfer learning paradigm U-Net architecture with VGG-19 net for the encoding part. We find that our model reaches a Structural Similarity (SSIM) index value close to 1 both for the test sample (SSIM = [Formula: see text]) and in 5-fold cross validation (SSIM = [Formula: see text]); the results are also shown to outperform classic denoising methods, in particular for images with lower SNR. The proposed work represents the first application of Artificial Intelligence in the field of CPI and demonstrates its high potential: speeding-up the acquisition by a factor 20 over the fastest CPI so far demonstrated, enabling recording potentially 200 volumetric images per second. The presented results open the way to scanning-free real-time volumetric imaging at video rate, which is expected to achieve a substantial influence in various applications scenarios, from monitoring neuronal activity to machine vision and security.

Correlated-photon imaging at 10 volumetric images per second

The correlation properties of light provide an outstanding tool to overcome the limitations of traditional imaging techniques. A relevant case is represented by correlation plenoptic imaging (CPI), a quantum-inspired volumetric imaging protocol employing spatio-temporally correlated photons from either entangled or chaotic sources to address the main limitations of conventional light-field imaging, namely, the poor spatial resolution and the reduced change of perspective for 3D imaging. However, the application potential of high-resolution imaging modalities relying on photon correlations is limited, in practice, by the need to collect a large number of frames. This creates a gap, unacceptable for many relevant tasks, between the time performance of correlated-light imaging and that of traditional imaging methods. In this article, we address this issue by exploiting the photon number correlations intrinsic in chaotic light, combined with a cutting-edge ultrafast sensor made of a large array of single-photon avalanche diodes (SPADs). This combination of source and sensor is embedded within a novel single-lens CPI scheme enabling to acquire 10 volumetric images per second. Our results place correlated-photon imaging at a competitive edge and prove its potential in practical applications.

Computational holographic ghost diffraction

Since the paradigm shift in 2009 from pseudo-thermal ghost imaging (GI) to computational GI using a spatial light modulator, computational GI has enabled image formation via a single-pixel detector and thus has a cost-effective advantage in some unconventional wave bands. In this Letter, we propose an analogical paradigm known as computational holographic ghost diffraction (CH-GD) to shift ghost diffraction (GD) from classical to computational by using self-interferometer-assisted measurement of field correlation functions rather than intensity correlation functions. More than simply "seeing" the diffraction pattern of an unknown complex volume object with single-point detectors, CH-GD can retrieve the diffracted light field's complex amplitude and can thus digitally refocus to any depth in the optical link. Moreover, CH-GD has the potential to obtain the multimodal information including intensity, phase, depth, polarization, and/or color in a more compact and lensless manner.

Computational version of the correlation light-field camera

Light-field cameras allow the acquisition of both the spatial and angular components of the light-field. The conventional way to perform such acquisitions leads to a strong spatio-angular resolution limitation but correlation-enabled plenoptic cameras have been introduced recently that relax this constraint. Here we use a computational version of this concept to acquire realistic light-fields images using a commercial DSLR Camera lens as an imaging system. By placing the image sensor in the focal plane of a lens, within the camera we ensure the acquisition of pure angular components together with the spatial information. We perform an acquisition presenting a high spatio-angular rays resolution obtained through a trade off of the temporal resolution. The acquisition reported is photo-realistic and the acquisition of diffraction limited features is observed with the setup. Finally, we demonstrate the refocusing abilities of the camera.

Light-field microscopy with correlated beams for high-resolution volumetric imaging

Light-field microscopy represents a promising solution for microscopic volumetric imaging, thanks to its capability to encode information on multiple planes in a single acquisition. This is achieved through its peculiar simultaneous capture of information on light spatial distribution and propagation direction. However, state-of-the-art light-field microscopes suffer from a detrimental loss of spatial resolution compared to standard microscopes. In this article, we experimentally demonstrate the working principle of a new scheme, called Correlation Light-field Microscopy (CLM), where the correlation between two light beams is exploited to achieve volumetric imaging with a resolution that is only limited by diffraction. In CLM, a correlation image is obtained by measuring intensity correlations between a large number of pairs of ultra-short frames; each pair of frames is illuminated by the two correlated beams, and is exposed for a time comparable with the source coherence time. We experimentally show the capability of CLM to recover the information contained in out-of-focus planes within three-dimensional test targets and biomedical phantoms. In particular, we demonstrate the improvement of the depth of field enabled by CLM with respect to a conventional microscope characterized by the same resolution. Moreover, the multiple perspectives contained in a single correlation image enable reconstructing over 50 distinguishable transverse planes within a 1 mm³ sample.

Refocusing Algorithm for Correlation Plenoptic Imaging

Correlation plenoptic imaging (CPI) is a technique capable of acquiring the light field emerging from a scene of interest, namely, the combined information of intensity and propagation direction of light. This is achieved by evaluating correlations between the photon numbers measured by two high-resolution detectors. Volumetric information about the object of interest is decoded, through data analysis, from the measured four-dimensional correlation function. In this paper, we investigate the relevant aspects of the refocusing algorithm, a post-processing method that isolates the image of a selected transverse plane within the 3D scene, once applied to the correlation function. In particular, we aim at bridging the gap between existing literature, which only deals with refocusing algorithms in case of continuous coordinates, and the experimental reality, in which the correlation function is available as a discrete quantity defined on the sensors pixels.

Effect of Finite-Sized Optical Components and Pixels on Light-Field Imaging through Correlated Light

Diffraction-limited light-field imaging has been recently achieved by exploiting light spatial correlations measured on two high-resolution detectors. As in conventional light-field imaging, the typical operations of refocusing and 3D reconstruction are based on ray tracing in a geometrical optics context, and are thus well defined in the ideal case, both conceptually and theoretically. However, some properties of the measured correlation function are influenced by experimental features such as the finite size of apertures, detectors, and pixels. In this work, we take into account realistic experimental conditions and analyze the resulting correlation function through theory and simulation. We also provide an expression to evaluate the pixel-limited resolution of the refocused images, as well as a strategy for eliminating artifacts introduced by the finite size of the optical elements.

Spectrally Gated Microscopy (SGM) with Meta Optics for Parallel Three-Dimensional Imaging

Volumetric imaging with high spatiotemporal resolution is of utmost importance for various applications ranging from aerospace and defense to real-time imaging of dynamic biological processes. To facilitate three-dimensional sectioning, current technology relies on mechanisms to reject light from adjacent out-of-focus planes either spatially or by other means. Yet, the combination of rapid acquisition time and high axial resolution is still elusive, motivating a persistent pursuit for emerging imaging approaches. Here we introduce and experimentally demonstrate a concept named spectrally gated microscopy (SGM), which enables a single-shot interrogation over the full axial dimension while maintaining a submicron sectioning resolution. SGM utilizes two important features enabled by flat optics (i.e., metalenses or diffractive lenses), namely, a short focal length and strong chromatic aberrations. Using SGM we demonstrate three-dimensional imaging of millimeter-scale samples while scanning only the lateral dimension, presenting a significant advantage over state-of-the-art technology.

Towards Quantum 3D Imaging Devices

We review the advancement of the research toward the design and implementation of quantum plenoptic cameras, radically novel 3D imaging devices that exploit both momentum–position entanglement and photon–number correlations to provide the typical refocusing and ultra-fast, scanning-free, 3D imaging capability of plenoptic devices, along with dramatically enhanced performances, unattainable in standard plenoptic cameras: diffraction-limited resolution, large depth of focus, and ultra-low noise. To further increase the volumetric resolution beyond the Rayleigh diffraction limit, and achieve the quantum limit, we are also developing dedicated protocols based on quantum Fisher information. However, for the quantum advantages of the proposed devices to be effective and appealing to end-users, two main challenges need to be tackled. First, due to the large number of frames required for correlation measurements to provide an acceptable signal-to-noise ratio, quantum plenoptic imaging (QPI) would require, if implemented with commercially available high-resolution cameras, acquisition times ranging from tens of seconds to a few minutes. Second, the elaboration of this large amount of data, in order to retrieve 3D images or refocusing 2D images, requires high-performance and time-consuming computation. To address these challenges, we are developing high-resolution single-photon avalanche photodiode (SPAD) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim to reduce both the acquisition and the elaboration time by two orders of magnitude. Routes toward exploitation of the QPI devices will also be discussed.

Correlation plenoptic imaging between arbitrary planes

We propose a novel method to perform plenoptic imaging at the diffraction limit by measuring second-order correlations of light between two reference planes, arbitrarily chosen, within the tridimensional scene of interest. We show that for both chaotic light and entangled-photon illumination, the protocol enables to change the focused planes, in post-processing, and to achieve an unprecedented combination of image resolution and depth of field. In particular, the depth of field results larger by a factor 3 with respect to previous correlation plenoptic imaging protocols, and by an order of magnitude with respect to standard imaging, while the resolution is kept at the diffraction limit. The results lead the way towards the development of compact designs for correlation plenoptic imaging devices based on chaotic light, as well as high-SNR plenoptic imaging devices based on entangled photon illumination, thus contributing to make correlation plenoptic imaging effectively competitive with commercial plenoptic devices.

Correlation Plenoptic Imaging: An Overview

Plenoptic imaging (PI) enables refocusing, depth-of-field (DOF) extension and 3D visualization, thanks to its ability to reconstruct the path of light rays from the lens to the image. However, in state-of-the-art plenoptic devices, these advantages come at the expenses of the image resolution, which is always well above the diffraction limit defined by the lens numerical aperture (NA). To overcome this limitation, we have proposed exploiting the spatio-temporal correlations of light, and to modify the ghost imaging scheme by endowing it with plenoptic properties. This approach, named Correlation Plenoptic Imaging (CPI), enables pushing both resolution and DOF to the fundamental limit imposed by wave-optics. In this paper, we review the methods to perform CPI both with chaotic light and with entangled photon pairs. Both simulations and a proof-of-principle experimental demonstration of CPI will be presented.

Using turbulence scintillation to assist object ranging from a single camera viewpoint

Image distortions caused by atmospheric turbulence are often treated as unwanted noise or errors in many image processing studies. Our study, however, shows that in certain scenarios the turbulence distortion can be very helpful in enhancing image processing results. This paper describes a novel approach that uses the scintillation traits recorded on a video clip to perform object ranging with reasonable accuracy from a single camera viewpoint. Conventionally, a single camera would be confused by the perspective viewing problem, where a large object far away looks the same as a small object close by. When the atmospheric turbulence phenomenon is considered, the edge or texture pixels of an object tend to scintillate and vary more with increased distance. This turbulence induced signature can be quantitatively analyzed to achieve object ranging with reasonable accuracy. Despite the inevitable fact that turbulence will cause random blurring and deformation of imaging results, it also offers convenient solutions to some remote sensing and machine vision problems, which would otherwise be difficult.