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

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.

High-resolution multi-z confocal microscopy with a diffractive optical element

There has been recent interest in the development of fluorescence microscopes that provide high-speed volumetric imaging for life-science applications. For example, multi-z confocal microscopy enables simultaneous optically-sectioned imaging at multiple depths over relatively large fields of view. However, to date, multi-z microscopy has been hampered by limited spatial resolution owing to its initial design. Here we present a variant of multi-z microscopy that recovers the full spatial resolution of a conventional confocal microscope while retaining the simplicity and ease of use of our initial design. By introducing a diffractive optical element in the illumination path of our microscope, we engineer the excitation beam into multiple tightly focused spots that are conjugated to axially distributed confocal pinholes. We discuss the performance of this multi-z microscope in terms of resolution and detectability and demonstrate its versatility by performing in-vivo imaging of beating cardiomyocytes in engineered heart tissues and neuronal activity in c. elegans and zebrafish brains.