Programmable array microscopes (PAMs) use "multi-pinhole" masks in confocal image planes to introduce illumination
and block the "out-of-focus light". Compared to traditional confocal microscopes (CM), PAM systems have higher
efficiency in utilizing the signal light and faster image acquisition speed. However, these advantages are gained at the
cost of using more complicated optics and detectors. Compressive sampling (CS) measurement patterns can be used as
pinhole masks in PAM systems. With CS patterns, the light collected after the detector mask can be summed up and
used to reconstruct the imaging scene via solving an l1-minimization problem. Only a simple relay-lens and a singlepixel
detector are needed to measure the intensity of the summed light. Therefore the optical complexity associated with
conventional PAM systems can be reduced. Since only a single-pixel detector is needed, this system can also be called a
single-pixel PAM or SP-PAM system. In this work, we introduce the design and fabrication of a prototype SP-PAM
system. In this system, scrambled-block Hadamard ensembles (SBHE) are used as CS measurement patterns and a
digital micromirror device (DMD) is employed to realize these patterns.
We describe the fabrication and characterization of a compressive-sampling multispectral imaging (CS-MSI) system that uses single-pixel detectors to capture multiple spectral images concurrently, without mechanical scanning. In particular, we pay special attention to the hardware/software characterization of the CS-MSI system, wherein we discuss the optical realization of the proposed system, measure the effective sensitivity range, and investigate the relationship between the digital micromirror device frame rate and the reconstruction quality. We also compare the imaging performance of the Hadamard-pace variable-density sampling method, which is the CS method implemented with our hardware architecture, with the conventional random sampling method. We propose a compressive-sampling modulation transfer function (CS-MTF) to measure the amplitude response of different spatial frequencies by using different CS methods.
Compressive imaging (CI) system is a novel electro-optical imaging system design, which uses a single-pixel photo
detector to capture two dimensional (2D) images. Instead of sampling the image directly by the sensor following the
classic Nyquist-Shannon sampling theorem, CI systems insert a measurement layer between the image formation and the
image recording media so that projection measurement matrices used to conduct compressive sampling can be
effectively introduced to the imaging process. The Digital Micromirror Device (DMD) can be used to implement the
projection measurement matrices. The imaging performance of a DMD based CI system relies more than just on the
imaging optics and the pixel size of the sensor. It also depends on the design of the measurement matrices and their
physical representations by the DMD. In the present work, we implemented three compressive sampling methods with
the DMD, namely the random basis under-sampling method, the random sampling method in the Hadamard space and
the variable density sampling method in the Hadamard space. We experimentally demonstrated that the design and
implementation of these methods have a direct impact on the imaging performance of the CI system. We tested the
system with different sampling ratios, DMD mirror configurations and imaging optics. Their influences on the
reconstructed image quality are demonstrated by experimental results. Lastly, we discussed the illumination issue of the
reconstructed image, which is not related to resolution, but is important for our visual perception of the reconstructed
image.
In this paper, a new approach for Confocal Microscopy (CM) based on the framework of compressive sensing is
developed. In the proposed approach, a point illumination and a random set of pinholes are used to eliminate
out-of-focus information at the detector. Furthermore, a Digital Micromirror Device (DMD) is used to efficiently
scan the 2D or 3D specimen but, unlike the conventional CM that uses CCD detectors, the measured data in
the proposed compressive confocal microscopy (CCM) emerge from random sets of pinhole illuminated pixels
in the specimen that are linearly combined (projected) and measured by a single photon detector. Compared
to conventional CM or programmable array microscopy (PAM), the number of measurements needed for nearly
perfect reconstruction in CCM is significantly reduced. Our experimental results are based on a testbed that uses
a Texas Instruments DMD (an array of 1024×768; 13.68×13.68 μm2 mirrors) for computing the linear projections
of illuminated pixels and a single photon detector is used to obtain the compressive sensing measurement. The
position of each element in the DMD is defined by the compressed sensing measurement matrices. Threedimensional
image reconstruction algorithms are developed that exploit the inter-slice spatial image correlation
as well as the correlation between different 2D slices. A comprehensive performance comparison between several
binary projection patterns is shown. Experimental and simulation results are provided to illustrate the features
of the proposed systems.
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