628 research outputs found
Compressive Holographic Video
Compressed sensing has been discussed separately in spatial and temporal
domains. Compressive holography has been introduced as a method that allows 3D
tomographic reconstruction at different depths from a single 2D image. Coded
exposure is a temporal compressed sensing method for high speed video
acquisition. In this work, we combine compressive holography and coded exposure
techniques and extend the discussion to 4D reconstruction in space and time
from one coded captured image. In our prototype, digital in-line holography was
used for imaging macroscopic, fast moving objects. The pixel-wise temporal
modulation was implemented by a digital micromirror device. In this paper we
demonstrate temporal super resolution with multiple depths recovery
from a single image. Two examples are presented for the purpose of recording
subtle vibrations and tracking small particles within 5 ms.Comment: 12 pages, 6 figure
Compact single-shot hyperspectral imaging using a prism
We present a novel, compact single-shot hyperspectral imaging method. It enables capturing hyperspectral images using a conventional DSLR camera equipped with just an ordinary refractive prism in front of the camera lens. Our computational imaging method reconstructs the full spectral information of a scene from dispersion over edges. Our setup requires no coded aperture mask, no slit, and no collimating optics, which are necessary for traditional hyperspectral imaging systems. It is thus very cost-effective, while still highly accurate. We tackle two main problems: First, since we do not rely on collimation, the sensor records a projection of the dispersion information, distorted by perspective. Second, available spectral cues are sparse, present only around object edges. We formulate an image formation model that can predict the perspective projection of dispersion, and a reconstruction method that can estimate the full spectral information of a scene from sparse dispersion information. Our results show that our method compares well with other state-of-the-art hyperspectral imaging systems, both in terms of spectral accuracy and spatial resolution, while being orders of magnitude cheaper than commercial imaging systems
A review of snapshot multidimensional optical imaging: Measuring photon tags in parallel
Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition–also dubbed snapshot imaging–has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications
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Sporadic absorption tomography using a conical shell X-ray beam
We demonstrate tomography by measuring a sporadic sequence of ring shaped projections collected during a translational scan. We show that projections using 10% sampling may be used to construct optical sections with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the order of 40 dB and 0.9, respectively. This relatively small degradation in image fidelity was achieved for a 90% potential reduction in X-ray dose coupled with a reduction in scan time. Our approach is scalable in both X-ray energy and inspection volume. A driver for our method is to complement previously reported conical shell beam techniques concerning the measurement of diffracted flux for structural analysis. This work is of great relevance to time critical analytical scanning applications in security screening, process control and diagnostic imaging
High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction
Spectral imaging is a fundamental diagnostic technique with widespread
application. Conventional spectral imaging approaches have intrinsic
limitations on spatial and spectral resolutions due to the physical components
they rely on. To overcome these physical limitations, in this paper, we develop
a novel multi-spectral imaging modality that enables higher spatial and
spectral resolutions. In the developed computational imaging modality, we
exploit a diffractive lens, such as a photon sieve, for both dispersing and
focusing the optical field, and achieve measurement diversity by changing the
focusing behavior of this lens. Because the focal length of a diffractive lens
is wavelength-dependent, each measurement is a superposition of differently
blurred spectral components. To reconstruct the individual spectral images from
these superimposed and blurred measurements, model-based fast reconstruction
algorithms are developed with deep and analytical priors using alternating
minimization and unrolling. Finally, the effectiveness and performance of the
developed technique is illustrated for an application in astrophysical imaging
under various observation scenarios in the extreme ultraviolet (EUV) regime.
The results demonstrate that the technique provides not only
diffraction-limited high spatial resolution, as enabled by diffractive lenses,
but also the capability of resolving close-by spectral sources that would not
otherwise be possible with the existing techniques. This work enables high
resolution multi-spectral imaging with low cost designs for a variety of
applications and spectral regimes.Comment: accepted for publication in IEEE Transactions on Computational
Imaging, see DOI belo
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