Plenoptic cameras enable capture of a 4D lightfield, allowing digital refocusing and depth estimation from data
captured with a compact portable camera. Whereas most of the work on plenoptic camera design has been
based a simplistic geometric-optics-based characterization of the optical path only, little work has been done of
optimizing end-to-end system performance for a specific application. Such design optimization requires design
tools that need to include careful parameterization of main lens elements, as well as microlens array and sensor
In this paper we are interested in evaluating the performance of a multispectral plenoptic camera, i.e. a camera
with spectral filters inserted into the aperture plane of the main lens. Such a camera enables single-snapshot
spectral data acquisition.1–3
We first describe in detail an end-to-end imaging system model for a spectrally coded plenoptic camera that we
briefly introduced in.4 Different performance metrics are defined to evaluate the spectral reconstruction quality.
We then present a prototype which is developed based on a modified DSLR camera containing a lenslet array
on the sensor and a filter array in the main lens. Finally we evaluate the spectral reconstruction performance of
a spectral plenoptic camera based on both simulation and measurements obtained from the prototype.
A new hyperspectral imaging system is constructed based on the idea of compressive sensing (CS). The compressed
hyperspectral measurements are acquired and unmixed directly with the proposed algorithm which determines the
abundance fractions of endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube
itself. Without the intermediate stage of 3D hyper-cube processing, data reconstruction and unmixing are combined into
a single step of much lower complexity. We assume that the involved endmembers' signatures are known and given,
from which we then directly compute abundances. We also extend this approach to blind unmixing where endmembers'
signatures are not precisely known a priori.
Building on the mathematical breakthroughs of compressive sensing (CS), we developed a 2D spectrometer system that
incorporates a spatial light modulator and a single detector. For some wavelengths outside the visible spectrum, when it
is too expensive to produce the large detector arrays, this scheme gives us a better solution by using only one pixel.
Combining this system with the "smashed filter" technique, we hope to create an efficient IR gas sensor. We performed
Matlab simulations to evaluate the effectiveness of the smashed filter for gas tracing.