In this article, the authors present a new approach to building simpler, smaller, and cheaper digital cameras that can operate efficiently across a broader spectral range than conventional silicon-based cameras. The approach fuses a new camera architecture based on a digital micromirror device with the new mathematical theory and algorithms of compressive sampling.

/pdf/single-pixel-imaging-via-compressive-sampling-17xma709hr.pdf

Single-Pixel Imaging via Compressive Sampling

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

pdf/medical-hyperspectral-imaging-a-review-2opknr5cow.pdf

Medical hyperspectral imaging: a review

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

Structured Compressed Sensing: From Theory to Applications

Apparatus and method for increasing the sensitivity in the detection of optical coherence tomography and low coherence interferometry (“LCI”) signals by detecting a parallel set of spectral bands, each band being a unique combination of optical frequencies. The LCI broad bandwidth source is split into N spectral bands. The N spectral bands are individually detected and processed to provide an increase in the signal-to-noise ratio by a factor of N. Each spectral band is detected by a separate photo detector and amplified. For each spectral band the signal is band pass filtered around the signal band by analog electronics and digitized, or, alternatively, the signal may be digitized and band pass filtered in software. As a consequence, the shot noise contribution to the signal is reduced by a factor equal to the number of spectral bands. The signal remains the same. The reduction of the shot noise increases the dynamic range and sensitivity of the system.

Apparatus and method for ranging and noise reduction of low coherence interferometry LCI and optical coherence tomography OCT signals by parallel detection of spectral bands

Until very recently, handheld spectrometers were the domain of major analytical and security instrument companies, with turnkey analyzers using spectroscopic techniques from X-ray fluorescence (XRF) for elemental analysis (metals), to Raman, mid-infrared, and near-infrared (NIR) for molecular analysis (mostly organics). However, the past few years have seen rapid changes in this landscape with the introduction of handheld laser-induced breakdown spectroscopy (LIBS), smartphone spectroscopy focusing on medical diagnostics for low-resource areas, commercial engines that a variety of companies can build up into products, hyphenated or dual technology instruments, low-cost visible-shortwave NIR instruments selling directly to the public, and, most recently, portable hyperspectral imaging instruments. Successful handheld instruments are designed to give answers to non-scientist operators; therefore, their developers have put extensive resources into reliable identification algorithms, spectroscopic libraries or databases, and qualitative and quantitative calibrations. As spectroscopic instruments become smaller and lower cost, “engines” have emerged, leading to the possibility of being incorporated in consumer devices and smart appliances, part of the Internet of Things (IOT). This review outlines the technologies used in portable spectroscopy, discusses their applications, both qualitative and quantitative, and how instrument developers and vendors have approached giving actionable answers to non-scientists. It outlines concerns on crowdsourced data, especially for heterogeneous samples, and finally looks towards the future in areas like IOT, emerging technologies for instruments, and portable hyphenated and hyperspectral instruments.

https://journals.sagepub.com/doi/pdf/10.1177/0003702818809719

EXPRESS: Portable Spectroscopy:

Encoded spatio-spectral information processing is performed using a system having a radiation source, wavelength dispersion device and two-dimensional switching array, such as digital micro-mirror array (DMA). In one aspect, spectral components from a sample are dispersed in space and modulated separately by the switching array, each element of which may operate according to a predetermined encoding pattern. The encoded spectral components can then be detected and analyzed. In a different aspect, the switching array can be used to provide a controllable radiation source for illuminating a sample with radiation patterns that have predetermined characteristics and separately encoded components. Various applications are disclosed.

System and method for encoded spatio-spectral information processing

An adaptive digitally tuned light source is disclosed, in the form of a de-dispersive imaging spectrograph in both the visible and near infrared spectral regions. The devices are capable of illuminating a sample with appropriate energy-weighted spectral bands or spatio-spectral bands that relate only to the constituents of interest. The energy from each of the spectral resolution elements can be digitally modulated to provide a tuned weighted spectral output. A tuned light source device based on the present disclosure can be adapted for use in a conventional imaging microscope system to enable direct measure of spatio-spectral features of interest.

Application of spatial light modulators for new modalities in spectrometry and imaging

A spectral measurement device comprising an entrance aperture for receiving an electromagnetic energy and a mask located at the entrance aperture in the form of a two-dimensional encodement pattern. An optical element conditions the electromagnetic energy received from the mask for presentation to the spectral dispersion element and the and a spectral dispersion element disperses the electromagnetic energy in one or more dimensions. Additionally, the optical element conditions the dispersed electromagnetic energy onto an array of detector elements.

Devices and method for spectral measurements

A hyper-spectral imaging system comprises imaging foreoptics (1020) to focus on a scene or object of interest (1010) and transfer the image of said scene or object (1010) onto the focal plane of a spatial light modulator (1030), a spatial light modulator (1030) placed at a focal plane of said imaging foreoptics (1020), an imaging dispersion device (1040) disposed to receive an output image of the spatial light modulator (1030), and an image collecting device disposed to receive the output of the imaging dispersion device (1040).

Hyper-spectral imaging methods and devices

A hyper-spectral analysis method for characterizing or distinguishing diverse elements within hyper-spectral images. A plurality of patches of pixels from within the hyper-spectral images are extracted as being patches around pixels of the elements to be characterized or distinguished. The statistics of spectra for each patch of pixels are computed. A first classifier is computed from frequency-wise standard deviation of the spectra in each patch and a set of second classifiers are computed from principal components of the spectral in each patch. A combined classifier is computed based on the output of the first classifier and at least one of the second classifiers. The elements are characterized or distinguished based on the output of at least one of the classifiers, preferably the combined classifier.

System and method for hyper-spectral analysis

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