Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.

Significance: Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery.

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g.

Broadband thermal imaging using meta-optics.

Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8-12 μm).

A compressive hyperspectral video imaging system using a single-pixel detector.

Capturing fine spatial, spectral, and temporal information of the scene is highly desirable in many applications. However, recording data of such high dimensionality requires significant transmission bandwidth. Current computational imaging methods can partially address this challenge but are still limited in reducing input data throughput.

Evolution of Mood Symptomatology Through the COVID-19 Pandemic: Findings From the CovidSense Longitudinal Study.

Background The severe acute respiratory syndrome coronavirus 2 global pandemic, with its associated coronavirus disease 2019 (COVID-19) illness, has led to significant mental, physical, social, and economic hardships. Physical distancing, isolation, and fear of illness have significantly affected the mental health of people worldwide. Several studies have documented the cross-sectional elevated prevalence of mental anguish, but due to the sudden nature of the pandemic, very few longitudinal studies have been reported, especially covering the first phase of the pandemic.

PS$^{2}$2 F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing.

We propose a compact snapshot monocular depth estimation technique that relies on an engineered point spread function (PSF). Traditional approaches used in microscopic super-resolution imaging such as the Double-Helix PSF (DHPSF) are ill-suited for scenes that are more complex than a sparse set of point light sources. We show, using the Cramér-Rao lower bound, that separating the two lobes of the DHPSF and thereby capturing two separate images leads to a dramatic increase in depth accuracy.

SASSI - Super-Pixelated Adaptive Spatio-Spectral Imaging.

We introduce a novel video-rate hyperspectral imager with high spatial, temporal and spectral resolutions. Our key hypothesis is that spectral profiles of pixels within each super-pixel tend to be similar. Hence, a scene-adaptive spatial sampling of a hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions.

On space-spectrum uncertainty analysis for spectrally programmable cameras.

We introduce and analyze the concept of space-spectrum uncertainty for certain commonly used designs of spectrally programmable cameras. Our key finding states that, it is not possible to simultaneously acquire high-resolution spatial images while programming the spectrum at high resolution. This phenomenon arises due to a Fourier relationship between the aperture used for resolving spectrum and its corresponding diffraction blur in the spatial image.

Cross-Scale Predictive Dictionaries.

Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a large number of atoms. In this paper, we incorporate additional structure on to dictionary-based sparse representations for visual signals to enable speedups when solving sparse approximation problems.