Coding for compressive focal tomography

Brady, David J.; Marks, Daniel L.

doi:10.1364/ao.50.004436

Cited by 13 publications

(8 citation statements)

References 47 publications

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“…The optical design for the COSMOS macroscope used a dualfocus lenslet array (Figure 1D), balancing high light throughput, long depth-of-field, ease of implementation, and resolution, with modest data processing requirements and reasonable system cost. Theoretical analysis demonstrated that, in terms of light collection, defocus, and extracted neuronal source signalto-noise ratio (SNR) across the extent of the curved window, the COSMOS macroscope design outperformed other potential solutions (Abrahamsson et al, 2013;Brady and Marks, 2011;Cossairt et al, 2013;Hasinoff et al, 2009;Levin et al, 2009;Schechner et al, 2007;Figure S2). Empirical comparisons demonstrated that a COSMOS macroscope, with focal planes offset by $600 mm (Figures 1E and 1F), outperformed a comparable conventional macroscope in terms of depth of field while maintaining equivalent light throughput (Figures 1G and 1H).…”

Section: Resultsmentioning

confidence: 99%

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions

Kauvar¹,

Machado²,

Yuen³

et al. 2020

Neuron

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Section: Resultsmentioning

confidence: 99%

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions

Kauvar¹,

Machado²,

Yuen³

et al. 2020

Neuron

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“…It is important to note, however, that the actual object information contained in the raw petapixel photon flux is always much less than the pixel limit in natural fields. Noting recent progress in using compressive sampling to reduce bandwidth and pixel sampling rates [9][10][11][12] in addition to coding spectral [13] and focal [14] degrees of freedom, one may reasonably expect physical layer compression to reduce the camera data load by 4-5 orders of magnitude. In combination with parallel read-out enabled by multiscale design, this suggests that full petapixel data cube sensitivity may eventually be achievable.…”

Section: Information Capacity and Multiscale Designmentioning

confidence: 99%

Petapixel photography and the limits of camera information capacity

Brady

Marks

Feller

et al. 2013

Computational Imaging XI

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The monochromatic single frame pixel count of a camera is limited by diffraction to the space-bandwidth product, roughly the aperture area divided by the square of the wavelength. We have recently shown that it is possible to approach this limit using multiscale lenses for cameras with space bandwidth product between 1 and 100 gigapixels. When color, polarization, coherence and time are included in the image data cube, camera information capacity may exceed 1 petapixel/second. This talk reviews progress in the construction of DARPA AWARE gigapixel cameras and describes compressive measurement strategies that may be used in combination with multiscale systems to push camera capacity to near physical limits.

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“…For example, we have shown that 3D hyperspectral [18], diffraction [19], and x-ray scatter [20,21] images may be reconstructed from 2D data. We have also analyzed compressive sampling for reconstruction of 3D objects with conventional optics [22]. Most recently, we have shown that 3D video data cubes may be constructed from 2D frames [23], thus using compressive tomography to perform the reconstruction with respect to time.…”

Section: Introductionmentioning

confidence: 99%

Compressed sampling strategies for tomography

Kaganovsky

Holmgren

et al. 2014

J. Opt. Soc. Am. A

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We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels. We propose a new approach based on quasi-random detector subsampling, whereas previous approaches only addressed subsampling with respect to source location (view angle). The performance of different sampling strategies is considered using object-independent figures of merit, and also based on reconstructions for specific objects, with synthetic and real data. The proposed approach can be implemented using a structured illumination of the interrogated object or the detector array by placing a coded aperture/mask at the source or detector side, respectively. Advantages of the proposed approach include (i) for structured illumination of the detector array, it leads to fewer detector pixels and allows one to integrate detectors for scattered radiation in the unused space; (ii) for structured illumination of the object, it leads to a reduced radiation dose for patients in medical scans; (iii) in the latter case, the blocking of rays reduces scattered radiation while keeping the same energy in the transmitted rays, resulting in a higher signal-to-noise ratio than that achieved by lowering exposure times or the energy of the source; (iv) compared to view-angle subsampling, it allows one to use fewer measurements for the same image quality, or leads to better image quality for the same number of measurements. The proposed approach can also be combined with view-angle subsampling.

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Coding for compressive focal tomography

Cited by 13 publications

References 47 publications

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions

Petapixel photography and the limits of camera information capacity

Compressed sampling strategies for tomography

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