We present the design of a multi-spectral imager built using the architecture of the single-pixel camera. The architecture is enabled by the novel sampling theory of compressive sensing implemented optically using the Texas Instruments DLP™ micro-mirror array. The array not only implements spatial modulation necessary for compressive imaging but also provides unique diffractive spectral features that result in a multi-spectral, high-spatial resolution imager design. The new camera design provides multi-spectral imagery in a wavelength range that extends from the visible to the shortwave infrared without reduction in spatial resolution. In addition to the compressive imaging spectrometer design, we present a diffractive model of the architecture that allows us to predict a variety of detailed functional spatial and spectral design features. We present modeling results, architectural design and experimental results that prove the concept.
This paper reports an overview of recent results in compressive imaging and detection using a single-pixel camera. These applications use a digital micromirror device to spatially modulate the light from an observed scene using binary sensing patterns. The patterns are obtained from a special Hadamard matrix that contains blocks of rows of which each has a common local signature pattern. The blocks partition the Hadamard spectrum, thus permitting analysis of the scene in terms of these local signature patterns. In contrast, Hadamard patterns are typically described in terms of their sequency, which is a global property of each individual row. The proposed local-signature, row-block point of view can be beneficial since it permits us to adaptively select the best blocks with which to sense the signal/scene of interest, or to select the best blocks based on a priori information. As a result, in imaging applications more fine-scale detail can be extracted from the scene, and in detection applications fewer false positives can result. Note, this signature row-block partitioning is a general mathematical technique that can be applied to the Kronecker product of any two matrices, of any size. For example, in our imaging application, we extend this idea to a Hadamard matrix that is not a power of two, yet whose block-signatures possess the familiar Sylvester-Walsh power-of-two sequency patterns.
The measurement efficiency of Compressive Sensing (CS) enables the computational construction
of images from far fewer measurements than what is usually considered necessary by the Nyquist–
Shannon sampling theorem. There is now a vast literature around CS mathematics and applications
since the development of its theoretical principles about a decade ago. Applications include quantum
information to optical microscopy to seismic and hyper-spectral imaging. In the application of
shortwave infrared imaging, InView has developed cameras based on the CS single-pixel camera
architecture. This architecture is comprised of an objective lens to image the scene onto a Texas
Instruments DLP® Micromirror Device (DMD), which by using its individually controllable
mirrors, modulates the image with a selected basis set. The intensity of the modulated image is then
recorded by a single detector.
While the design of a CS camera is straightforward conceptually, its commercial implementation
requires significant development effort in optics, electronics, hardware and software, particularly if
high efficiency and high-speed operation are required. In this paper, we describe the development of
a high-speed CS engine as implemented in a lab-ready workstation. In this engine, configurable
measurement patterns are loaded into the DMD at speeds up to 31.5 kHz. The engine supports
custom reconstruction algorithms that can be quickly implemented. Our work includes optical path
design, Field programmable Gate Arrays for DMD pattern generation, and circuit boards for front
end data acquisition, ADC and system control, all packaged in a compact workstation.
Obtaining high frame rates is a challenge with compressive sensing (CS) systems that gather measurements in a
sequential manner, such as the single-pixel CS camera. One strategy for increasing the frame rate is to divide the
FOV into smaller areas that are sampled and reconstructed in parallel. Following this strategy, InView has
developed a multi-aperture CS camera using an 8×4 array of photodiodes that essentially act as 32 individual
simultaneously operating single-pixel cameras. Images reconstructed from each of the photodiode measurements are
stitched together to form the full FOV.
To account for crosstalk between the sub-apertures, novel modulation patterns have been developed to allow
neighboring sub-apertures to share energy. Regions of overlap not only account for crosstalk energy that would
otherwise be reconstructed as noise, but they also allow for tolerance in the alignment of the DMD to the lenslet
array.
Currently, the multi-aperture camera is built into a computational imaging workstation configuration useful for
research and development purposes. In this configuration, modulation patterns are generated in a CPU and sent to
the DMD via PCI express, which allows the operator to develop and change the patterns used in the data acquisition
step. The sensor data is collected and then streamed to the workstation via an Ethernet or USB connection
for the reconstruction step. Depending on the amount of data taken and the amount of overlap between sub-apertures,
frame rates of 2–5 frames per second can be achieved. In a stand-alone camera platform, currently in
development, pattern generation and reconstruction will be implemented on-board.
Images from a novel shortwave infrared (SWIR, 900 nm to 1.7 μm) camera system are presented. Custom electronics
and software are combined with a digital micromirror device (DMD) and a single-element sensor; the latter are
commercial off-the-shelf devices, which together create a lower-cost imaging system than is otherwise available in this
wavelength regime. A compressive sensing (CS) encoding schema is applied to the DMD to modulate the light that has
entered the camera. This modulated light is directed to a single-element sensor and an ensemble of measurements is
collected. With the data ensemble and knowledge of the CS encoding, images are computationally reconstructed. The
hardware and software combination makes it possible to create images with the resolution of the DMD while employing
a substantially lower-cost sensor subsystem than would otherwise be required by the use of traditional focal plane arrays
(FPAs). In addition to the basic camera architecture, we also discuss a technique that uses the adaptive functionality of
the DMD to search and identify regions of interest. We demonstrate adaptive CS in solar exclusion experiments where
bright pixels, which would otherwise reduce dynamic range in the images, are automatically removed.
A method for designing a signal-adapted, two-channel biorthogonal wavelet filter bank that maximizes coding gain is presented using the technique of Pseudoframes for Subspaces (PFFS). At the same time the PFFS model is able to incorporate the requirements of regularity and perfect reconstruction into this design. The coding gain achieved for AR(2) signals can result in a reduction in bit rate of more than 0.66 bits per sample as compared to traditional biorthogonal wavelet filter banks of the same length. The ability for PFFS to combine all of these design elements using an unconstrained optimization parameter makes pursuing this technique worthwhile.
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