Computed tomography imaging spectrometer by using a novel hybrid diffractive-refractive element

Computed Tomography Imaging Spectrometer (CTIS) systems are snapshot hyperspectral imaging devices capable of capturing dense spectra of static as well as dynamic scenes. A three-dimensional hyperspectral cube is smeared across the spatial dimension via Diffractive Optical Element (DOE) and projected across multiple angles forming a two-dimensional compressed sensor image. In this paper we demonstrate material characterization and classification capability of a compact CTIS system leveraging spectral signatures. Then we propose an approach to simultaneously reconstruct and segment into regions corresponding to different materials hyperspectral images with enhanced spatial resolution from CTIS sensor measurements.

We have constructed a portable computed tomography imaging spectrometer (CTIS) based on a previous laboratory-bench version. This spectrometer features a computer-generated phase-only hologram as the dispersive element and collects spatial and spectral data from the diffracted orders. CTIS is capable of flash spectral imaging. Reconstruction of the image cube from raw data is achieved by computed-tomography techniques. Other improvements from the original design include a more modular design, an automated and more precise calibration technique, and the inclusion of constraints in the image cube reconstruction. Reconstruction results compare favorably with measurements by a fiber spectrometer. Keywords: Imaging spectrometry, computed tomography, flash spectral imaging.

We have constructed a computed-tomography imaging spectrometer that uses a phase-only computer-generated hologram (CGH) array illuminator as the disperser. This imaging spectrometer collects multiplexed spatial and spectral data simultaneously and can be used for flash spectral imaging. The CGH disperser has been designed to maintain nearly equal spectral diffraction efficiency among a 5 x 5 array of diffraction orders and to minimize diffraction efficiency into higher orders. Reconstruction of the (x, y, lambda) image cube from the raw, two-dimensional data is achieved by computed-tomography techniques. The reconstructed image and spectral-signature data compare favorably with measurements by other spectrometric methods.

We present results of field evaluations of computed tomography imaging spectrometer (CTIS) systems, using an adaptive optics (AO) compensated telescope, for applications in astronomical multispectral imaging and imaging spectroscopy in both visible (0.4 - 0.7 μm) and near infrared (1.2 - 2.1 μm) bands. We develop and analyze a convolution model for this hyperspectral imaging method which includes a convolution representation of the effects of the AO system. We use expectation maximization, with additional prior constraints derived from the physics of the instrumental, statistical and atmospheric effects inherent in such observations, to extract details of features in the hyperspectral images. Data for this study were obtained using the Advanced Electro-Optics System (AEOS) -- a facility of the Air Force Maui Space Surviellance System.

The Computed Tomography Imaging Spectrometer (CTIS) is a snapshot hyperspectral camera based on computational imaging. Typical designs use a Keplerian beam expander to limit the imaged scene and decrease the incident ray angles. We have found that the use of a Galilean beam expander instead can be beneficial. Often a smaller system with a better optical quality is achievable with the disadvantage of a vignetted image. Results of a comparison between both designs based on a prototype will be shown. Furthermore, we present our miniaturized CTIS system, which uses the Galilean design.

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

Hyperspectral imaging provides complex image data with spectral information from many fluorescent species contained within the sample such as the fluorescent labels and cellular or pigment autofluorescence. To maximize the utility of this spectral imaging technique it is necessary to couple hyperspectral imaging with sophisticated multivariate analysis methods to extract meaningful relationships from the overlapped spectra. Many commonly employed multivariate analysis techniques require the identity of the emission spectra of each component to be known or pure component pixels within the image, a condition rarely met in biological samples. Multivariate curve resolution (MCR) has proven extremely useful for analyzing hyperspectral and multispectral images of biological specimens because it can operate with little or no a priori information about the emitting species, making it appropriate for interrogating samples containing autofluorescence and unanticipated contaminating fluorescence. To demonstrate the unique ability of our hyperspectral imaging system coupled with MCR analysis techniques we will analyze hyperspectral images of four-color in-situ hybridized rat brain tissue containing 455 spectral pixels from 550 - 850 nm. Even though there were only four colors imparted onto the tissue in this case, analysis revealed seven fluorescent species, including contributions from cellular autofluorescence and the tissue mounting media. Spectral image analysis will be presented along with a detailed discussion of the origin of the fluorescence and specific illustrations of the adverse effects of ignoring these additional fluorescent species in a traditional microscopy experiment and a hyperspectral imaging system.

DMP-Net: Deep semantic prior compressed spectral reconstruction method towards intraoperative imaging of brain tissue.

Hyperspectral and multispectral imaging for evaluating food safety and quality

Hyperspectral imaging (HSI) shows great promise for the detection and classification of several diseases, particularly in the fields of optical biopsy as applied to oncology, and functional retinal imaging in ophthalmology. In this paper, we discuss the application of HSI to the detection of retinal diseases and technological solutions that address some of the fundamental difficulties of spectral imaging within the eye. HSI of the retina offers a route to non-invasively deduce biochemical and metabolic processes within the retina. For example it shows promise for the mapping of retinal blood perfusion using spectral signatures of oxygenated and deoxygenated hemoglobin. Compared with other techniques using just a few spectral measurements, it offers improved classification in the presence of spectral cross-contamination by pigments and other components within the retina. There are potential applications for this imaging technique in the investigation and treatment of the eye complications of diabetes, and other diseases involving disturbances to the retinal, or optic-nerve-head circulation. It is well known that high-performance HSI requires high signal-to-noise ratios (SNR) whereas the application of any imaging technique within the eye must cope with the twin limitations of the small numerical aperture provided by the entrance pupil to the eye and the limit on the radiant power at the retina. We advocate the use of spectrally-multiplexed spectral imaging techniques (the traditional filter wheel is a traditional example). These approaches enable a flexible approach to spectral imaging, with wider spectral range, higher SNRs and lower light intensity at the retina than could be achieved using a Fourier-transform (FT) approach. We report the use of spectral imaging to provide calibrated spectral albedo images of healthy and diseased retinas and the use of this data for screening purposes. These images clearly demonstrate the ability to distinguish between oxygenated and deoxygenated hemoglobin using spectral imaging and this shows promise for the early detection of various retinopathies.

Spectroradiometric imaging is based on the fact that all materials display a specific spectral signature when light impinges on them. That is, they reflect, absorb, and emit electromagnetic waves in a particular pattern depending on the wavelength, leading to a spectrum for the material that can be used to classify it. An imaging spectroradiometer captures images where the spectral information of every pixel is collected for a set of contiguous discrete spectral bands. It is customary to classify these instruments as ultraspectral, hyperspectral, multispectral, and panchromatic imagers. Typically, ultraspectral data sets are made up of thousands of spectral bands, while hyperspectral sets have hundreds, multispectral tens, and panchromatic sets just a few. Thus, each ultraspectral or hyperspectral image contains large amounts of data which can be viewed as a cube with two spatial and one spectral dimension. Regardless of the application of the images, the analysis methods employed must deal with large quantities of data efficiently [1]. Originally, imaging spectroradiometers were used as airborne or spaceborne remote sensing instruments. During the last decade, spectral imaging has become a common advanced tool for remote sensing of the surface of Earth. It is being used to collect crucial data about many activities of economic or scientific relevance [2] as, for instance, mineral exploitation [3], agriculture and forestry use [4], environmental pollution [5], ecosystem studies [6], assessment of natural resources [7], water resource management [8], monitoring of coastal zones [9] and many others. For all these remote sensing applications the hyperspectral images obtained usually contain mixtures of spectra in every pixel due to the poor spatial resolution of the images when taken a large distance from the target. Thus a single pixel could typically correspond to one hundred or more square meters of land. As a result, most analysis methods developed had as their main objective to provide the segmentation of the images in terms of the ratio of endmembers present in every pixel to improve the spatial discrimination of these systems when analyzing different types of land covers [10].

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the , and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the , , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

As applied to a maskless laser lithography writing system Heidelberg DWL 66+ and the photoresist-chromium technology, we have found the tolerances on fabricating the binary-phase Dammann gratings having 7x7 useful diffraction orders and a structure separable in X and Y components and illuminated by a beam with a wavelength of 633 nm. It has been found that the obtained tolerances guarantee the production of Dammann gratings with a total diffraction efficiency close to the theoretical one and a root-mean-square error of departure of the intensity of useful diffraction orders of less than 10%.

LADMM-Net: An unrolled deep network for spectral image fusion from compressive data

Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera based on diffractive optic arrays. This approach to hyperspectral imaging has been demonstrated in all three infrared bands SWIR, MWIR and LWIR. The hyperspectral optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of this infrared hyperspectral sensor. This new and innovative approach to an infrared hyperspectral imaging spectrometer uses micro-optics that are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a small satellite, mini-UAV, commercial quadcopter or man portable. Also, an application of how this spectral imaging technology can easily be used to quantify the mass and volume flow rates of hydrocarbon gases. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. The detector array is divided into sub-images covered by each lenslet. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the number of simultaneous different spectral images collected each frame of the camera. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each frame. This system spans the SWIR and MWIR bands with a single optical array and focal plane array.