mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.Comment: This paper will appear in PNAS Nexu

Locus filters

In this paper, directly following from Gage [J. Opt. Soc. Am. 23, 46(1993) ], we study the design of a particular theoretical filter for photography, that we call the locus filter. It is built in such a way that a Wien-Planckian light (of any temperature) is spectrally mapped to another Wien-Planckian light. We provide a physical basis for designing such a filter based on the Wien approximation of Planck’s law, and we prove that there exists a unique set of filters that have the desired property. While locus filtered Wien-Planckian lights are on the locus, the amount they shift depends both on the locus filter used and on the color temperature of the light. In experiments, we analyze the nature of temperature change when applying different locus filters and we show that real lights shift more or less as if they were Planckians in terms of the changes in their correlated color temperatures. We also study the quality of the filtered light in terms of distance from the Planckian locus and color rendering index

Finding a Colour Filter to Make a Camera Colorimetric by Optimisation

The Luther condition states that a camera is colorimetric if its spectral sensitivities are a linear transform from the XYZ colour matching functions. Recently, a method has been proposed for finding the optimal coloured filter that when placed in front of a camera, results in effective sensitivities that satisfy the Luther condition. The advantage of this method is that it finds the best filter for all possible physical capture conditions. The disadvantage is that the statistical information of typical scenes are not taken into account. In this paper we set forth a method for finding the optimal filter given a set of typical surfaces and lights. The problem is formulated as a bilinear least-squares estimation problem (linear both in the filter and the colour correction). This is solved using Alternating Least-Squares (ALS) technique. For a range of cameras we show that it is possible to find an optimal colour correction filter with respect to which the cameras are almost colorimetric