Enhancement of CASSI by a zero-order image employing a single detector

Coded aperture snapshot spectral imaging (CASSI) makes it possible to recover 3D hyperspectral data from a single 2D image. However, the reconstruction problem is severely underdetermined and efforts to improve the compression ratio typically make the imaging system more complex and cause a significant loss of incoming light intensity. In this paper, we propose a novel approach to CASSI which enables capturing both spectrally sheared and integrated image of a scene with a single camera. We performed hyperspectral imaging of three different testing scenes in the spectral range of 500-900 nm. We demonstrate the prominent effect of using the non-diffracted image on the reconstruction of data from our camera. The use of the spectrally integrated image improves the reconstruction quality and we observed an approx. fivefold reduction in reconstruction time

Mejoramiento de la resolución espectral de imágenes hiperespectrales, por medio de un sistema óptico compresivo de múltiple-apertura

El sistema de sensado de imágenes espectrales, basado en la apertura codificada y de única toma (CASSI), captura la información espacial y espectral de una escena; mediante mediciones codificadas aleatorias capturadas en un sensor 2D. Un algoritmo basado en la teoría de sensado compresivo (CS), es utilizado para recuperar la escena tridimensional original a partir de las mediciones aleatorias capturadas. La calidad de reconstrucción de la escena depende exclusivamente, de la matriz de sensado del CASSI, la cual es determinada por la estructura de las aperturas codificadas que son utilizadas.En este artículo, se propone una generalización del sistema CASSI por medio del desarrollo de un sistema óptico multi-apertura, que permite el mejoramiento de la resolución espectral. En el sistema propuesto, un par de aperturas codificadas de alta resolución es introducido en el sistema CASSI, permitiendo así, la codificación tanto espacial como espectral de la imagen hiperespectral. Este enfoque permite la reconstrucción de cubos de datos hiperespectrales, donde el número de las bandas espectrales se aumenta significativamente respecto al original, y la calidad espacial es mejorada en gran medida. Así mismo, los experimentos simulados muestran mejoramiento en la relación de pico-de-señal-a-ruido (PSNR), junto con un mejor ajuste en las firmas espectrales reconstrui-das sobre los datos espectrales originales.The Coded Aperture Snapshot Spectral Imaging (CASSI) system captures the three-dimensional (3D) spatio-spectral information of a scene using a set of two-dimensional (2D) random-coded Focal Plane Array (FPA) measurements. A compressive sensing reconstruc-tion algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the structure of a set of random coded apertures. In this paper, the CASSI system is generalized by developing a multiple-aperture optical imaging system such that spectral resolution en-hancement is attainable. In the proposed system, a pair of high-resolution coded apertures is introduced into the CASSI system, allow-ing it to encode both spatial and spectral characteristics of the hyperspectral image. This approach allows the reconstruction of super-resolved hyperspectral data cubes, where the number of spectral bands is significantly increased and the quality in the spatial domain is greatly improved. Extensively simulated experiments show a gain in the peak-signal-to-noise ratio (PSNR), along with a better fit of the reconstructed spectral signatures to the original spectral data

A review of snapshot multidimensional optical imaging: Measuring photon tags in parallel

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition–also dubbed snapshot imaging–has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications

CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration

Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is typically performed via electronically controllable spatial light modulators (SLM). An HR image is then reconstructed from the encoded measurements by solving an inverse problem that involves the forward model of the imaging system. To capture system non-idealities such as optical aberrations, a mainstream approach is to conduct an offline calibration scan to measure the system response for a point source at each spatial location on the imaging grid. However, it is challenging to run calibration scans when using structured SLMs as they cannot encode individual grid locations. In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that locomotes a pre-printed fixed coded aperture. A deep neural network is then leveraged to correct for the influences of system non-idealities in multiplexed measurements without the need for offline calibration scans. Finally, a deep plug-and-play algorithm is used to reconstruct images from corrected measurements. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity

A review of snapshot multidimensional optical imaging: Measuring photon tags in parallel

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition–also dubbed snapshot imaging–has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications

The development of optical projection tomography instrumentation and its application to in vivo three dimensional imaging of zebrafish

OPT is a three dimensional (3D) imaging technique that can produce 3D reconstructions of transparent samples, requiring only a widefield imaging system and sample rotation. OPT can be readily applied to chemically cleared samples, or to live transparent organisms such as nematodes or zebrafish. For preclinical imaging, there is a trade-off between optical accessibility and biological relevance to humans. Adult Danio rerio (zebrafish) represent a promising compromise, with greater homology to humans than smaller animals, and superior optical accessibility than mice. However, their size and physiology present a number of imaging challenges including non-negligible absorption and optical scattering, and limited time for image data acquisition if the fish are to be recovered for longitudinal studies. A key goal of this PhD thesis research was to develop OPT to address these challenges and improve in vivo imaging capabilities for this model organism. This thesis builds on previous work at Imperial where angularly multiplexed OPT using compressed sensing was developed and applied to in vivo imaging of a cancer-burdened adult zebrafish, with a sufficiently short OPT data acquisition time to allow recovery of the fish after anaesthesia. The previous cross-sectional study of this work was extended to a longitudinal study of cancer progression that I undertook. The volume and quality of data acquired in the longitudinal study presented a number of data processing challenges, which I addressed with improved automation of the data processing pipeline and with the demonstration that convolutional neural networks (CNN) could replace the iterative compressed sensing algorithm previously used to suppress artifacts when reconstructing undersampled OPT data sets. To address the issue of high attenuation through the centre of an adult zebrafish, I developed conformal-high-dynamic-range (C-HDR) OPT and demonstrated that it could provide sufficient dynamic range for brightfield imaging of such optically thick samples, noting that transmitted light images can provide anatomical context for fluorescence image data. To reduce the impact of optical scattering in OPT, I developed a parallelised quasi-confocal version of OPT called slice-illuminated OPT (slice-OPT) to reject scattered photons and demonstrated this with live zebrafish. To enable 3D imaging with short wave infrared (SWIR) light, without the requirement of an expensive Ge or InGaAs camera, I implemented a single pixel camera and demonstrated single-pixel OPT (SP-OPT) for the first time.Open Acces