An emerging and developing paradigm in BioPhotonics concerns the Bio-lensing effect, that is, living organism as biological cells or their inner structures can be modeled as pure-phase objects with optical and photonics properties typical of micro-lenses. Nowadays micro-lenses are ubiquitous and a lot of effort is put into developing low-cost and low-impact fabrication processes and for searching smart and biocompatible materials. The lensing effect of different types of cells and sub-cellular compartments has been recently demonstrated generating a great impact. Indeed, the dimensions of biolenses allows to surpass all the issues related to the scaling down of the fabrication processes and, also, their natural origin solves the problem of material compatibility necessary for biomedical applications. Here, we will show the recent achievements based on the bio-lensing properties of different cells and organelles ranging from imaging, super-resolution, cell clustering and lithography.

In this work we propose an inverse problems based iterative reconstruction method for tomographic diffractive microscopy, involving measurements in off-axis configuration. More precisely, we propose a strategy that aims to eliminate reconstruction errors that can be caused by perturbations in the illumination wave of the reference arm. Our original contribution is to build the inverse problem considering as unknowns both the targeted 3D sample map and the perturbation map, that are jointly reconstructed and unmixed during the iterative process. This self-calibration process is rendered possible by the multiplicity of sample observations from multiple views, where the reference perturbed background remains invariant. We validate the feasibility of our approach on reconstructions from simulated data under different experimental conditions.

Fourier ptychographic microscopy is a recent imaging technique that overcomes the limitations of conventional optics. The images it produces are particularly fine and super-resolved. They are also very rich, since they are bimodal (intensity and phase images) compared with conventional microscopy. FPM therefore holds great promise for a whole range of medical applications.

In this work, the potential of this microscopy is explored by considering the biological application of automatic diagnosis of malaria on a stained blood smear. We report that an appreciable improvement in the classification of parasitized red blood cells is obtained when intensity and phase images are jointly exploited in a deep convolutionnal neural network, compared to that obtained with intensity images alone. We also show that such joint exploitation considerably relaxes the constraints relative to the choice of microscope objective. In particular, an objective lens with a numerical aperture as low as 0.2 can be used with little degradation in classification performance. The performances obtained are close to those obtained with a conventional resolution microscope equiped with a 0.9 numerical aperture objective. This can be highly desirable for the realization of rapid diagnostic system, which requires access to large fields of view.

Digital pathology indicates the ensemble of emerging methods, analysis techniques, and diagnostic protocols that aim to improve and automatize the inspection of biological samples such as slides of histological tissues and blood smears. From the microscopy methods standpoint, accessing a wide field of view (FOV) is highly required to look at the big picture of tissue organization, structures and connections. At the same time, high resolution is required to inspect the slide at the single cell, organelle and membranes level. Here, we show the use of Fourier Ptychographic Microscopy (FPM) to image effectively large tissue slides portions, over a wide mm² FOV, while attaining submicron resolution. Resolution is improved up to a six-fold factor by probing the specimen from multiple directions and exploiting a synthetic aperture principle. An iterative phase retrieval algorithm estimated the high-resolution phase-contrast from a set of low-resolution intensities. Remarkably, FPM is a marker-free and quantitative method. Neural and kidney tissue slides, both unstained and marked with different stains are imaged by FPM to investigate the effect of staining on the phase-contrast images. We show novel tissue analysis and classification techniques working on FPM images. These are specifically tailored for exploiting the multi-scale imaging capabilities of FPM and can be used to classify tissue portions working across the scales, i.e. analysing the phase-contrast signatures at the tissue organization level up to the single membrane level. We apply this novel approach to classify tissue slides from patients with breast cancer and fibroadenoma tissue.

This study explores the advancements in Computational Imaging (C.I.) systems, which transcend conventional imaging methodologies by integrating innovative hardware and reconstruction algorithms. Focusing on the challenge of balancing the field of view (FOV) and resolution in microscopy due to sensor size limitations, we have introduced the Multiple Point Impulse Response (MPIR) as a Point Spread Function (PSF) engineering technique aimed at extending the FOV in our previous work. Despite encountering scrambled images, the utilization of sparse recovery algorithms successfully reconstructs object information beyond the sensor’s active area. Additionally, increasing impulse density improves reconstruction quality, although practical sensor constraints, notably limited bit depth, impact contrast, and overall reconstruction quality. Here, we have shown that with a trade-off in image fidelity and data information content, it is possible to overcome the limitation imposed by the sensor bit-depth.

Various modes of spatial modulations in compressive imaging can vastly affect the measurement quality and reduce the number of measurements needed. While there have been experiments focusing on different modulations, usually only a handful have been tested and in varying conditions. We propose a scheme unifying the testing parameters while exploiting our dual hyperspectral compressive microscope to create a range of conditions in one measurement. In it, the influence of light collection from the DMD, including its diffraction, can be considered. With this, we proceeded to test a total of 20 modulations and their modifications. Simulations using the same parameters were conducted with and without added noise. In the experimental setup, all modulations have been tested across three samples and three different resolutions to provide a comprehensive comparison. Individual effects and complications have also been identified, providing tips for modulation testing and deployment.

Co-design consists of optimizing the parameters of the lens and the processing together to obtain a gain in performance for the entire optical/processing chain, which requires new optimization tools, as traditional optical design ones can no longer be used easily. In the state of the art, joint optical/processing optimization methods based on statistical scene and blur models and analytical performance models have been proposed, and recently, new approaches based on the joint optimization of a neural network and optical components with a large database have been developed. However, to the best of our knowledge, no comparison of co-design results using either a model-based or a data-based approach for the same task have been conducted, which is the scope of this paper. We consider here the optimization of phase masks to extend the camera depth of field. We compare the optimization results using a performance model based on restoration error using either a generalized Wiener filter, or a neural network. We investigate the optimization trend depending on the neural network complexity, the starting point of the optimization and the possible interaction between the two approaches.

PISTIL interferometry (PISton and TILt interferometry), dedicated to the measurement of regular segmented wave surfaces such as segmented mirrors or the near-field output of coherent combined lasers, consists on making replicas of the central part of the wavefronts of each surface segment interfere with its nearest neighbors. The resulting interference pattern is made of an apposition of surfaces containing two-wave interference whose phase, frequency and orientation provide direct information on the respective heights and tilts of the segments in relation to each other. This technique has evolved into a new device called Full Light PISTIL. More compact and more efficient, this new version overcomes the drawbacks of previous PISTIL interferometer version by taking into account the whole surface of the incident wavefront. The performance obtained, estimated on a segmented mirror, is state of the art, and points the way towards a plug and play product adapted to the specific measurement needs of these surfaces, even in low light conditions.

In this study we have developed a compact and versatile phase camera functioning as a wavefront sensor for macroscopy or microscopy applications. This device records two intensity images at different focal points and, with the integration of an electrically tunable lens (ETL), operates in real time. Working with intensity images allows achieving high resolutions, near the actual CCD/CMOS sensor resolution. Here we show the application of the camera in two very different scenarios, a macroscopic application, where the camera was coupled with a simple lens relay to study the behavior of a deformable mirror (DM); and characterize defocus and astigmatism in optical lenses. On the second example, the camera was attached directly to a microscope using a simple c-mount to follow human blood moving in real time.

Tomographic diffraction microscopy is a 3D marker-less imaging technique allowing for quantitative characterization of the complex refractive index of the investigated sample. The method is a generalization of digital holography with a full control of the sample’s illumination angle. Combining several holographic acquisitions through a 3D synthetic aperture process makes it possible to assess the optical field scattered by a 3D object. Recently we proposed to take benefits of the democratization of polarization array sensors to characterize birefringent samples. With this presentation, we demonstrate that using the same experimental configuration, with a modified demosaicking procedure, we can produce 3D Differential Interference Contrast microscope images of isotrope sample.

This study introduces an innovative approach to volumetric polarimetry, introducing a method that combines Gabor inline holography with conventional polarimetric setups to compute the complete Mueller Matrix of a sample. The study includes the validation of the technique through calibration targets and extends to complex volumetric samples, showcasing the potential of this method for characterizing intricate polarization properties in three-dimensional specimens.

In this study, we introduce a technique for creating 3D speckle patterns, which can be utilized in photoluminescence tomography based the single-pixel camera technique. Initially, we showcase our capability to generate independent speckle patterns at different planes near the focal plane, facilitating z-axis resolution imaging. Using simulated data, we explored the effects of various factors and, informed by these analyses, developed an experimental setup capable of producing 3D speckle patterns in reality. Our results indicate that by precisely adjusting the phase distribution of the input field, we can reproduce the 3D patterns with the level of simulation-experiment correlation exceeding 0.9. We further demonstrate the application of actual 3D speckle patterns in 3D photoluminescence tomography using a single-pixel camera and discuss its promise for investigating defects in optical materials.

Many current biomedical applications require the development of advanced non-linear optical microscopy methods to provide functional and non-invasive imaging of live tissues with sub-cellular resolution. In the first part of the talk I will present recent advances in label-free metabolic imaging of living tissues by two-photon fluorescence lifetime microscopy (2P-FLIM) of endogenous biomarkers. We implement simultaneous two-photon excitation of NAD(P)H and FAD by wavelength mixing to acquire 2P-FLIM data of the two biomarkers at the same time and perform efficient multiparametric metabolic imaging in dynamic biological system. We demonstrate how 2P-FLIM of the two metabolic coenzymes reveals the richness and complexity of several metabolic processes in intact tissues with minimal phototoxicity and can be implemented non-invasively for longitudinal studies in vitro and in vivo such as stem cell differentiation, T cell activation and embryo development. In the second part of the talk I will present recent advances in polarization resolved second harmonic generation (pSHG) and modelling from protein molecular structure. We establish a general multi-scale numerical framework relating the micrometer-scale SHG measurements at the optical wavelength to the atomic-scale and molecular structure of the proteins under study and their supramolecular arrangement.

Optical imaging and beam delivery deep into biological tissue is a great challenge due to strong light scattering. It has been shown in the last years that wavefront shaping employing a digital spatial light modulator (SLM) has the power to exploit the scattered light to enhance the penetration depth. Typically, a guide star emitting a known wavefront is required to probe scattering events and determine the necessary mask to be displayed. The resulting Peak-to-Background Ratio (PBR) is commonly used as a quality measure for the Digital Optical Phase Conjugation (DOPC) procedure. Here, we introduce a new correction quality criterion to evaluate the measured phase mask. By placing the SLM into the object beam, it is possible to manipulate the phase measurement. By first measuring the required mask and then exposing a second hologram with the conjugated measured phase displayed on the SLM, we can cancel out the distortion. We demonstrate that this cancellation can be observed in the angular spectrum of the hologram, enabling evaluation of the mask without considering any playback properties. Furthermore, it corresponds to better playback efficiency.

A model to achieve high-resolution three-dimensional microscopic images from synthetically generated digital holograms by using Convolutional Neural Networks (CNNs) is proposed. By employing low-cost microscopy systems and computational techniques, we demonstrate that proposed model provides viable alternative to costly high-resolution microscopic systems. Specifically, the study focuses on the elimination of the unwanted terms in backward-propagated holograms to closely approximate original high-resolution objects.

In the tumor microenvironment, cell interactions play a crucial role in influencing the morphology and metastasis characteristics of non-cancerous and cancerous cells. Although machine learning techniques have been proven effective in classifying individually cultured cell lines, their accuracy may deteriorate in classifying a co-cultured mixture of cells. In the proposed work, the optical path difference images of human dermal fibroblast and melanoma A375 cells were recorded, individually and in a mixture, using the off-axis digital holographic microscopy setup. A dataset consisted of segmented and labeled images of both sample types. The dataset was used to train a convolutional neural network through transfer learning to extract the morphology relevant features. An XGBoost classifier trained on the extracted features is found to effectively recognize morphological changes in cells within a mixture and classify them accurately with a limited size dataset.

We investigate an unconventional concept of near-eye display based on sparse aperture imaging principles. In this original approach, a pixel is projected onto the retina through the emission of emissive points randomly distributed on the surface of the glass. In this contribution, we evaluate the impact of the emissive point distribution characteristics on the imaging performances. We highlight the central role of the emissive point density on the contrast of the image projected onto the retinal plane. This result leads us to reconsider our initial display architecture.

The diffraction limit is a fundamental barrier in optical microscopy, restricting the smallest resolvable feature size of a microscopic imaging system. Microsphere-assisted super-resolution microscopy has emerged as a promising approach for overcoming this limit. This technique offers several advantages, including no need of fluorescent dyes, easy operation under white light illumination, and good compatibility with commercial optical microscopes. Various strategies have been proposed to enhance the imaging performance of microspheres. Recently, patchy microspheres were found to exhibit super-resolution capabilities in air with an enhanced imaging contrast. In this work, we studied the super-resolution imaging performance of patchy microspheres fully immersed in liquid. Furthermore, we demonstrated the formation of photonic hooks from patchy microspheres within a liquid environment. The findings of this study will extent the application of patchy microspheres from air to liquid immersion mode, opening up new possibilities for super-resolution imaging in liquid environment with patchy microspheres.

We increase single-photon LiDAR capabilities via a hardware-accelerating inpainting transformer model. This model reconstructs all non-observed information within the image plane as it communicates with the beam steering hardware. We apply this to 3D time-of-flight (ToF) reconstruction, where objects obstruct each other’s line of sight. We use ToF histograms to distinguish objects within either the foreground and background, and their overlap will be treated as the dynamic mask for the model to reconstruct. We also employ this to unorthodox scanning patterns such as Lissajous and spiral, which are riddled with sparsity. Lastly, we are developing an AI MEMs system, which intelligently downsamples the image plane based off foreground masks, combating sampling redundancy. We believe that our approach will be useful in applications for imaging and sensing dynamic targets with sparse single-photon data across all domains.

This work presents a new burst mode CMOS image sensor in 0.35 μm SiGe BiCMOS technology that can achieve a pixel rate of 1 TS/s. The sensor employs a novel integrated Streak architecture that includes a vector of 200 integrated photodiodes, each connected to a wideband transimpedance amplifier, and a 200 points deep analogue on-chip memory for burst imaging. Placing the pixel electronics next to the photodector results in a high fill factor of 84 %. The circuit has a closed loop delay generator that allows sampling speeds from 50 μs to 200 ps, resulting in the largest range recorded for a monolithic CMOS sensor to date. The sensor features a post-trigger functionality to avoid synchronization issues during event recording. For the recording of repetitive events, the sensor has a new accumulation mode to enhance the signal to noise ratio (SNR) by reducing the bandwidth of the sample and hold circuit, thus allowing the SNR to be increased by a factor of sqrt(10) per decade. The state-of-the-art time resolution makes this sensor ideal for observing subnanosecond events. It finds applications in various fields, including fluorescence metrology, time-resolved spectroscopy, optical tomography, laser Doppler velocimetry, and detonics.

Biologically inspired event-based vision sensors (EVS) are growing in popularity due to performance benefits including ultra-low power consumption, high dynamic range, data sparsity, and fast temporal response. They efficiently encode dynamic information from a visual scene through pixels that respond autonomously and asynchronously when the per-pixel illumination level changes by a user-selectable contrast threshold ratio, θ. Due to their unique sensing paradigm and complex analog pixel circuitry, characterizing Event-based Vision Sensor (EVS) is non-trivial. The step-response probability curve (S-curve) is a key measurement technique that has emerged as the standard for measuring θ. Though the general concept is straightforward, obtaining accurate results requires a thorough understanding of pixel circuitry and non-idealities to correctly obtain and interpret results. Furthermore, the precise measurement procedure has not been standardized across the field, and resulting parameter estimates depend strongly on methodology, measurement conditions, and biasing – which are not generally discussed. In this work, we detail the method for generating accurate S-curves by applying an appropriate stimulus and sensor configuration to decouple 2nd-order effects from the parameter being studied. We use an EVS pixel simulation to demonstrate how noise and other physical constraints can lead to error in the measurement, and develop two techniques that are robust enough to obtain accurate estimates. We then apply best practices derived from our simulation to generate S-curves for the latest generation Sony IMX636 and interpret the resulting family of curves to correct the apparent anomalous result of previous reports suggesting that θ changes with illumination. Further, we demonstrate that with correct interpretation, fundamental physical parameters such as dark current and RMS noise can be accurately inferred from a collection of S-curves, leading to more accurate parameterization for high-fidelity EVS simulations.

A novel single-pixel fluorescence microscope with super-resolution, based on structured illumination microscopy, has been developed to release the full potential of single-pixel microscopy. By employing enhanced spatially resolved patterns, the proposed microscope measures light intensities using a bucket detector and improves lateral resolution for both brightfield and fluorescence imaging by extending the spatial frequency spectrum. The results demonstrate significant improvements in lateral resolution, highlighting its application in fluorescence microscopy.

We tested the ability of multiphoton microscopy (MPM) devices to produce label-free images of the virus Sars-Cov-2 responsible for the Covid global health crisis of the years 2020-2022. The virion cultures were grown in our high-security virology laboratory and then optically inactivated so as to preserve the integrity of the virus without its contagious nature. We used magnetic beads for concentrating the particles and increasing the viral quantity. We have determined a spectral range of excitation and emission, combined with adapted energy levels for the non-destructive excitation and efficient collection. We obtained a discriminating signature of these viral particles thanks to the nonlinear optical process involved in MPM and produced an image of virion bundles concentrated around magnetic particles. We have confirmed the origin of the MPM images from the SARS-Cov-2 viral particles thanks to electron microscopy images. This super-resolution imaging solution has allowed to reveal the shape of viral particles of SARS-Cov-2 in their entirety, combined in clusters around magnetic particles, the core of which is clearly identified by its diameter of 200 nm. We attributed the origin of the optical signature from the virions to the presence of a large quantity of genetic material in the free viral particle. This substance, the DNA or RNA, is made up of an assembly of nucleic acids with a known nonlinear optical property. We are confident that the assembly of these formally non-linear elements enabled the creation of the multiphoton microscopy signal.

Broadband CARS is commonly used to study tissue sections of biological samples utilizing the inherent vibrational contrast present due to molecular vibrations. This technique however, is notably hindered by the ubiquitous non-resonant background (NRB) that plagues the interpretation of images. Nevertheless, a promising avenue is polarization suppression which was previously reported for single frequency CARS and has shown efficacy in NRB removal. Here, we employ polarization suppression using two acquisitions for interferometric NRB rejection, which has previously been applied to polymers and liquids. The spectral interferometric method requires only passive polarization optics, in addition to a BCARS system. The method was applied to the BCARS imaging of fungal spores and compared to the spectra obtained from the Kramers-Kronig phase retrieval algorithm.

Photobleaching is a light-induced effect where a fluorophore loses its fluorescence property due to light-induced damage. Controlling photons of light inside the scattering media is a challenging task due to refractive index inhomogeneity, multiple scattering of light, and formation of speckle noise. In this work, we have digitally transferred the coherent light of 0.74 mW power and concentrated the photon intensity in a controlled manner using the principle of constructive interference at the target location inside a tissue media, which is stained with fluorescence for generating pathological signals. The localized fluorescence signal extraction from the deep chicken tissue has been demonstrated with the developed binary-phase modulation based wavefront shaping system. The low-intensity coherent light has been re-localized from a large field of view to a spatio-temporal focused point, and fluorescence light emission has been detected from the fluorophores embedded in chicken tissue even after being exposed to light for a few hours. The fluorescence emission photons are obtained from the tissue by the developed experimental setup in the reflection mode.

High-accurate unwrapped phases are demanded in various research fields such as optical imaging optical holography, optical diffraction tomography, and magnetic resonance imaging. However, the ground-truth phase is not accessible due to 2 ambiguity which arises from phase jumps in the wrapped phase. In this study, we propose to improve the accuracy of unwrapping process by increasing the sampling frequency to reconstruct the unwrapped phase with high accuracy for the application of optical imaging. The simulation results show increasing the optical magnification from 4X to 8X enables improvement of the phase estimation accuracy by 51% for a highly refractive object. Experimental results validate the sensitivity of phase estimation on the sampling size. Our approach demonstrates significant achievement in obtaining ground-truth phases for highly refractive objects.

Hyperspectral imaging allows to reconstruct both spatial and quasi-continuous spectral information of an object (i.e., an hypercube), unlike multispectral imaging where the spectrum is divided in few bands. This work shows the innovating combination of single-pixel hyperspectral imaging with reflective-mode optical microscopy to retrieve high-spatial-resolution hypercubes of samples in the visible and in the near-infrared ranges. The spatial resolution reaches around 2.5 μm and the spectral information is sampled over around 200 spectral bands. Furthermore, statistical algorithms enables the clustering of regions of interest of inorganic and organic samples and the identification of spectral responses of targeted cells.

Differential Interference Contrast (DIC) microscopy is a label-free technique crucial for visualizing transparent biological specimens. In this study, we introduce a novel DIC interferometry imaging technique based on calcite beam displacer (CBD), significantly enhancing resolution and contrast. Our system, requires only two polarizers and a calcite beam displacer, offers a practical and straightforward implementation on standard microscopes. We demonstrate its efficacy by imaging human red blood cells (RBCs) and bacteria, retrieving differential phase information, and recording the dynamics of U2OS cells over an hour at 15 frames per second (fps). This advancement in DIC microscopy holds promise for broader applications in high-resolution imaging of transparent specimens in cell biology research.

Deep co-design methods have been proposed to optimize simultaneously optical and neural network parameters for many separate tasks such as high dynamic range, extended depth of field (EDOF), depth from defocus (DfD), object detection or pose estimation. In contrast, we study the multi-task co-design of an imaging system for two antagonist tasks: EDOF and DfD. We model and optimize a chromatic Cooke triplet using differentiable ray tracing, and we compare the performances for DfD and EDOF tasks, in a single, parallel and collaborative optimization scheme. We show how one task can benefit from the result of the other task. We also explore the benefit of the local positional information to process images with spatially varying point spread functions related to optical field aberrations.

Thanks to some technical progress in filter array technologies, capturing multimodal data of a scene, such as polarization and/or spectral information, in a single acquisition is possible. Nevertheless, a reconstruction procedure referred to as demosaicing is required to produce the various full definition images in each band. The computational imaging community often needs full-reference images to assess the performance of these reconstruction algorithms. Nevertheless, these multidimensional data are increasingly complex to capture, as the number of channels increases in the image. This often leads to misalignment among channels or noise introduced by imperfect optics. In this work, we propose a study on the use of these imperfect data in the context of demosaicing. The impact of misalignment is assessed on an existing Color Polarization Filter Array database, from which we demosaic the data using three types of demosaicing algorithms and use either pre-processed or raw dataset. We found that denoising and registration do not modify the hierarchy of best performing algorithms in case of sub-pixel shifts. We also show that visual artifacts, usually attributed to drawbacks of training-based demosaicing algorithms, may instead be due to the use of unregistered images during the training stage of the algorithms.

X-ray propagation-based phase contrast imaging (XPCI) relies on the coherence of the X-ray beam to achieve contrast from phase shift by letting the beam propagate in free space, hence yielding a Fresnel or Fraunhofer diffraction pattern. This contrast regime arises in high resolution imaging, where it is used for tomography in a wide range of applications. The exploitation of such images requires a phase retrieval step, which has proven sensitive to noise in low spatial frequencies. It is thought that incoherent scattering in the sample might contribute to this noise. Therefore, several approaches to combine phase contrast and incoherent scattering have recently been proposed. To this aim, we propose a new way to simulate phase contrast based on the Wigner Distribution Function (WDF). In this framework, the exit wave of the sample is calculated through ray-tracing, which would allow accounting for effects including refraction and reflection. The interference is then calculated in the exit plane using the WDF, instead of in the detector plane, as is the case using classical methods. Images can then be simulated photon by photon, by first simulating incoherent scattering in the sample using a Monte Carlo particle transport code, followed by diffraction by probability sampling of the WDF. As a first demonstration of the framework, we simulate the double-slit experiment, as well as a variant with a scatterer in one of the slits. Since the double-slit has an analytical solution for the WDF, both in its standard form and with different amplitudes in each slit, this enables us to bypass the most challenging numerical difficulties for this initial demonstration.

Spectral imaging is a technique that captures spectral information from a scene and maps it onto a 2D image, featuring the potential to reveal hidden features and properties of objects that are invisible to the human eye, such as elemental and molecular compositions. Augmented reality (AR), on the other hand, is a technology that enhances the perception of reality by superimposing digital information on the physical world. While these technologies have different purposes, they can be considered one and the same in terms of providing an user-centric extension of reality. Spectral imaging provides the information that can reveal the underlying nature of objects, while AR provides the method of visualization that can display the information in an intuitive and interactive way. In this work, we present a novel Unity toolkit that combines spectral imaging and a HoloLens 2 AR device to create an interactive and immersive experience for the user. The toolkit enables the interactive visualization of various elemental maps of a 3D rock model in AR using a simple and intuitive interface. With this technique, the user can select a sample model and an elemental map from a preloaded asset library and then see the map projected onto the rock model in AR, using simple interactions such as zoom adjustment, rotation, and pan of the models to explore features and properties in detail. The toolkit offers several advantages, including better contextual interpretation of the spectral data by placing it in relation to the shape and texture of the rock, increased user engagement and curiosity through the creation of a realistic and immersive experience, and ease of decision-making through the provision of comparative tools. In short, by combining spectral imaging and AR, we present an innovative approach that can enrich the user experience and expand the user knowledge of the environment.

Stellar intensity interferometry (SII) is based on the correlation of the light intensity fluctuations of a star detected at two or more telescopes, with no need to combine the collected photons directly. A measurement of the correlation in full ”photon-counting mode” was experimented with fast photon counters in Italy (2016-2020) and is currently being adapted to the ASTRI Mini-Array. Performing image synthesis with ”photon-counting” SII requires a series of preparatory activities that involve the optimization of the pipelines for the treatment of time series acquired at extremely high photon rates, the development of efficient and innovative algorithms for the cross-correlation of the arrival times in large time series and the development of a preliminary version of a dedicated pipeline for the synthesis of images starting from interferometric data. Here we present the project and the present status of the activities.

In this study we analyze the effect of experimental errors on the optimization and calibration method of a Mueller matrix imaging polarimeter based on liquid crystal variable retarders. The study is carried out through numerical simulations, where the optimized Mueller matrix polarimeter is simulated considering misalignments of the polarimetric components, and variations in the induced retardance of the LCVRs. However, the final measurement error does not depend only on non-ideal elements, but also depends on the noise, in the irradiance measurements, and the accuracy of the calibration method. Thus, the eigenvalue calibration method is used in the simulations, including random variations in the irradiance matrix. The tolerances of the optimization and calibration method are analyzed, and the results are presented.

Time-domain optical coherence tomography (TD-OCT) is an advanced imaging technique that utilizes low-coherence interferometry to capture high-resolution, cross-sectional images of biological tissue. Conventional techniques used for endoscopy are invasive and is a highly discomforting and painful procedure for the patient. So, we have proposed a TDOCT based non-invasive technique that has demonstrated significant utility in providing real-time, high-resolution imaging of tissue microstructures within the gastrointestinal tract. The TD-OCT is an in-vivo and ex-vivo cross-sectional 3D imaging technique that works on the fundamental principle of light reflection and diffraction, in other words it can be equivalent to ultrasound. The major components of the proposed TD-OCT for endoscopy are probe, broadband light source, reference arm, sample arm, detector, and signal processing algorithm. A broadband light source, typically a super luminescent diode (SLD) or a femtosecond laser is used that sends light into biological tissue, where the light is partially reflected or backscattered by various tissue structures. Then we deployed the interferometry technique, which measures the time delay between the reference light and the backscattered light to create detailed cross-sectional images. The whole optical setup of TD-OCT is simulated in the Zemax Optic Studio software for the graphical and numerical validation of ray tracing, lateral resolution, depth of focus, optical path difference and coherent power. Therefore, the suggested TDOCT, offering non-invasive imaging with high cellular resolution, holds substantial promise for a range of applications, including ophthalmology, pathology, surgery, and endoscopy.