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.

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.

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.