Dual-camera design for hyperspectral and panchromatic imaging, using a wedge shaped liquid crystal as a spectral multiplexer

A Triangular-Matrix-Based Spectral Encoding Method for Broadband Filtering and Reconstruction-Based Spectral Measurement

Broadband filtering and reconstruction-based spectral measurement represent a hot technical route for miniaturized spectral measurement; the measurement encoding scheme has a great effect on the spectral reconstruction fidelity. The existing spectral encoding schemes are usually complex and hard to implement; thus, the applications are severely limited. Considering this, here, a simple spectral encoding method based on a triangular matrix is designed. The condition number of the proposed spectral encoding system is estimated and demonstrated to be relatively low theoretically; then, verification experiments are carried out, and the results show that the proposed encoding can work well under precise or unprecise encoding and measurement conditions; therefore, the proposed scheme is demonstrated to be an effective trade-off of the spectral encoding efficiency and implementation cost.

Eliminating Temporal Illumination Variations in Whisk-broom Hyperspectral Imaging

We propose a method for eliminating the temporal illumination variations in whisk-broom (point-scan) hyperspectral imaging. Whisk-broom scanning is useful for acquiring a spatial measurement using a pixel-based hyperspectral sensor. However, when it is applied to outdoor cultural heritages, temporal illumination variations become an issue due to the lengthy measurement time. As a result, the incoming illumination spectra vary across the measured image locations because different locations are measured at different times. To overcome this problem, in addition to the standard raster scan, we propose an additional perpendicular scan that traverses the raster scan. We show that this additional scan allows us to infer the illumination variations over the raster scan. Furthermore, the sparse structure in the illumination spectrum is exploited to robustly eliminate these variations. We quantitatively show that a hyperspectral image captured under sunlight is indeed affected by temporal illumination variations, that a Naïve mitigation method suffers from severe artifacts, and that the proposed method can robustly eliminate the illumination variations. Finally, we demonstrate the usefulness of the proposed method by capturing historic stained-glass windows of a French cathedral.

Beyond Tristimulus Color Vision with Perovskite-Based Multispectral Sensors

In this study, optical multispectral sensors based on perovskite semiconductors have been proposed, simulated, and characterized. The perovskite material system combined with the 3D vertical integration of the sensor channels allow for realizing sensors with high sensitivities and a high spectral resolution. The sensors can be applied in several emerging areas, including biomedical imaging, surveillance, complex motion planning of autonomous robots or vehicles, artificial intelligence, and agricultural applications. The sensor elements can be vertically integrated on a readout electronic to realize sensor arrays and multispectral digital cameras. In this study, three- and six-channel vertically stacked perovskite sensors are optically designed, electromagnetically simulated, and colorimetrically characterized to evaluate the color reproduction. The proposed sensors allow for the implementation of snapshot cameras with high sensitivity. The proposed sensor is compared to other sensor technologies in terms of sensitivity and selectivity.

Interference Spectral Imaging Based on Liquid Crystal Relaxation and Its Application in Optical Component Defect Detection

In this paper, we propose a fast interference spectral imaging system based on liquid crystal (LC) relaxation. The path delay of nematic LC during falling relaxation is used for the scanning of the optical path. Hyperspectral data can be obtained by Fourier transforming the data according to the path delay. The system can obtain two-dimensional spatial images of arbitrary wavelengths in the range of 300–1100 nm with a spectral resolution of 262 cm−1. Compared with conventional Fourier transform spectroscopy, the system can easily collect and integrate all valid information within 20 s. Based on the LC, controlling the optical path difference between two orthogonally polarized beams can avoid mechanical movement. Finally, the potential for application in contactless and rapid non-destructive optical component defect inspection is demonstrated.

Compressive spectral image fusion via a single aperture high throughput imaging system

Spectral image fusion techniques combine the detailed spatial information of a multispectral (MS) image and the rich spectral information of a hyperspectral (HS) image into a high-spatial and high-spectral resolution image. Due to the data deluge entailed by such images, new imaging modalities have exploited their intrinsic correlations in such a way that, a computational algorithm can fuse them from few multiplexed linear projections. The latter has been coined compressive spectral image fusion. State-of-the-art research work have focused mainly on the algorithmic part, simulating instrumentation characteristics and assuming independently registered sensors to conduct compressed MS and HS imaging. In this manuscript, we report on the construction of a unified computational imaging framework that includes a proof-of-concept optical testbed to simultaneously acquire MS and HS compressed projections, and an alternating direction method of multipliers algorithm to reconstruct high-spatial and high-spectral resolution images from the fused compressed measurements. The testbed employs a digital micro-mirror device (DMD) to encode and split the input light towards two compressive imaging arms, which collect MS and HS measurements, respectively. This strategy entails full light throughput sensing since no light is thrown away by the coding process. Further, different resolutions can be dynamically tested by binning the DMD and sensors pixels. Real spectral responses and optical characteristics of the employed equipment are obtained through a per-pixel point spread function calibration approach to enable accurate compressed image fusion performance. The proposed framework is demonstrated through real experiments within the visible spectral range using as few as 5% of the data.

Compressive spectral image reconstruction using deep prior and low-rank tensor representation

Compressive spectral imaging (CSI) has emerged as an alternative spectral image acquisition technology, which reduces the number of measurements at the cost of requiring a recovery process. In general, the reconstruction methods are based on handcrafted priors used as regularizers in optimization algorithms or recent deep neural networks employed as an image generator to learn a non-linear mapping from the low-dimensional compressed measurements to the image space. However, these deep learning methods need many spectral images to obtain good performance. In this work, a deep recovery framework for CSI without training data is presented. The proposed method is based on the fact that the structure of some deep neural networks and an appropriated low-dimensional structure are sufficient to impose a structure of the underlying spectral image from CSI. We analyzed the low-dimensional structure via the Tucker representation, modeled in the first net layer. The proposed scheme is obtained by minimizing the ${\ell _2}$-norm distance between the compressive measurements and the predicted measurements, and the desired recovered spectral image is formed just before the forward operator. Simulated and experimental results verify the effectiveness of the proposed method for the coded aperture snapshot spectral imaging.