US20060239336A1 - Method and Apparatus for Compressive Imaging Device - Google Patents

Method and Apparatus for Compressive Imaging Device Download PDF

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US20060239336A1
US20060239336A1 US11/379,688 US37968806A US2006239336A1 US 20060239336 A1 US20060239336 A1 US 20060239336A1 US 37968806 A US37968806 A US 37968806A US 2006239336 A1 US2006239336 A1 US 2006239336A1
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image
reconstruction
signal
matrices
acquiring
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Richard Baraniuk
Dror Baron
Marco Duarte
Ilan Goodman
Don Johnson
Kevin Kelly
Courtney Lane
Jason Laska
Dharmpal Takhar
Michael Wakin
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William Marsh Rice University
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Priority to EP06751035.4A priority patent/EP1880524B1/fr
Priority to PCT/US2006/015170 priority patent/WO2006116134A2/fr
Publication of US20060239336A1 publication Critical patent/US20060239336A1/en
Assigned to WILLIAM MARSH RICE UNIVERSITY reassignment WILLIAM MARSH RICE UNIVERSITY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LANE, COURTNEY C, KELLY, KEVIN F, TAKHAR, DHARMPAL, BARON, DROR Z, GOODMAN, ILAN N, WAKIN, MICHAEL B, DUARTE, MARCO F, LASKA, JASON N, BARANIUK, RICHARD, JOHNSON, DON H
Priority to US12/791,171 priority patent/US8199244B2/en
Priority to US12/792,336 priority patent/US20100315513A1/en
Priority to US13/462,212 priority patent/US8848091B2/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/20—Repeater circuits; Relay circuits
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N3/00—Scanning details of television systems; Combination thereof with generation of supply voltages
    • H04N3/02—Scanning details of television systems; Combination thereof with generation of supply voltages by optical-mechanical means only
    • H04N3/08—Scanning details of television systems; Combination thereof with generation of supply voltages by optical-mechanical means only having a moving reflector

Definitions

  • NSF grant CCF-0431150 ONR grant N00014-02-1-0353, and AFOSR grant FA9550-04-1-014.
  • the invention relates to imaging devices such as cameras, video cameras, microscopes, and other visualization techniques, and more particularly, to the acquisition of images and video using fewer measurements than previous techniques.
  • This process has two major shortcomings. First, acquiring large amounts of raw image or video data (large N) can be expensive, particularly at wavelengths where CMOS or CCD sensing technology is limited. Second, compressing raw data can be computationally demanding, particularly in the case of video. While there may appear to be no way around this procedure of “sample, process, keep the important information, and throw away the rest,” a new theory known as Compressive Sensing (CS) has emerged that offers hope for directly acquiring a compressed digital representation of a signal without first sampling that signal. See Candès, E., Romberg, J., Tao, T., “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform.
  • CS Compressive Sensing
  • the hardware designed for these purposes uses concepts that include optical projections, group testing (see Cormode, G., Muthukrishnan, S., “Towards an algorithmic theory of compressed sensing,” DIMACS Tech. Report 2005-40 (2005)), and signal inference.
  • Two notable previous DMD-driven applications involve confocal microscopy (Lane, P. M., Elliott, R. P., MacAulay, C. E., “Confocal microendoscopy with chromatic sectioning,” Proc. SPIE. Volume 4959 (2003) 23-26) and micro-optoelectromechanical (MOEM) systems (DeVerse, R. A., Coifman, R. R., Coppi, A. C., Fateley, W.
  • MOEM micro-optoelectromechanical
  • the present invention overcomes shortcomings of the prior approaches. Preferred embodiments of the present invention take fewer measurements than prior techniques, enable significant reduction in the resources (power, computation) required for visualization and use only a small number of physical sensors. The reduction in the size of the hardware associated with preferred embodiments of the invention further may significantly reduce costs of visualization systems.
  • the present invention can also acquire and process streaming video data (time-varying images). Finally, the present invention can adjust its data acquisition rate according to the amount of activity in the scene it is imaging.
  • the present invention uses algorithms and hardware to support a new theory of Compressive Imaging (CI).
  • the approach is based on a new digital image/video camera that directly acquires random projections without first collecting the N pixels/voxels.
  • CI Compressive Imaging
  • the image can be reconstructed, exactly or approximately, from these random projections by using a model, in essence to find the best or most likely image (in some metric) among all possible images that could have given rise to those same measurements. While several preferred embodiments of reconstruction are described below, it should be understood that additional techniques using or incorporating the present invention can also be used.
  • a small number of detectors, even a single detector, can be used.
  • the camera can be adapted to image at wavelengths of electromagnetic radiation that are currently impossible with conventional CCD and CMOS imagers. This feature is particularly advantageous, because in some cases the usage of many detectors is impossible or impractical, whereas the usage of a small number of detectors, or even a single detector, may become feasible using compressive imaging.
  • a camera in accordance with the present invention can also be used to take streaming measurements of a video signal, which can then be recovered using CS techniques designed for either 2-dimensional (2D) frame-by-frame reconstruction or joint 3D reconstruction. This allows a significant reduction in the computational complexity of the video encoding process.
  • An imaging system in accordance with the present invention enjoys a number of desirable features:
  • FIG. 1 is a diagram of a compressive imaging camera in accordance with a preferred embodiment of the present invention.
  • FIG. 2 is a diagram showing the results obtained via various imaging techniques.
  • FIG. 3 is a diagram showing frames from a sample video sequence obtained and reconstructed using various techniques
  • FIG. 4 is a flow diagram showing how a system in accordance with a preferred embodiment of the present invention determines the value of the optical inner product.
  • FIG. 5 is a flow diagram showing how a system in accordance with a preferred embodiment of the present invention obtains a reconstruction of an optical signal.
  • FIG. 6 describes a Texas Instruments digital micromirror device (DMD).
  • FIG. 6 ( a ) illustrates two mirrors and the mechanism that controls their tilts. A small tilting yoke, address electrodes, torsion hinges, and landing electrodes are created to control the mirror tilts. An array of such mirrors is shown in FIG. 6 ( b ).
  • FIGS. 7 ( a )-( c ) show two possible embodiments between the micromirror and photodiode.
  • the protrusions would act as incoherent scatters and should only shift the overall background while the main contribution to the encoded signal on the photodiode comes from the unperturbed mirror pixels.
  • the second, off-center configuration illustrated in FIG. 7 ( c ) would attempt to increase the contrast ratio by reflecting the light from the mirror into the photodiode at a more oblique angle.
  • a camera architecture of the present invention uses for random measurements a digital micromirror array to spatially modulate an incident image and reflecting the result to a lens, which focuses the light to a single photodiode for measurement.
  • these measurements correspond to inner products of the incident image with a sequence of pseudorandom patterns.
  • sparsity or compressibility that is, that there exists some basis, frame, or dictionary (possibly unknown at the camera) in which the image has a concise representation.
  • this system and method uses the above model (sparsity/compressibility) and some recovery algorithm (based on optimization, greedy, iterative, or other algorithms) to find the sparsest or most compressible or most likely image that explains the obtained measurements.
  • the use of sparsity for signal modeling and recovery from incomplete information are the crux of the recent theory of Compressive Sensing (CS), explained below.
  • the camera does not have to rely on reflecting light off a digital micromirror device as in FIG. 1 . See FIG. 4 .
  • Examples of systems that can modulate lightfields include digital micromirror devices, LCD shutter arrays (as in an LCD laptop projector), physically moving shutter arrays, any material that can be made more and less transparent to the lightfield of interest at different points in space, etc.
  • Compressive Sensing builds upon a core tenet of signal processing and information theory: that signals, images, and other data often contain some type of structure that enables intelligent representation and processing.
  • Current state-of-the-art compression algorithms employ a decorrelating transform to compact a correlated signal's energy into just a few essential coefficients.
  • Such transform coders exploit the fact that many signals have a sparse representation in terms of some basis A, meaning that a small number K of adaptively chosen transform coefficients can be transmitted or stored rather than N signal samples, where K ⁇ N.
  • a video sequence is a sequence of images, or a 3D signal.
  • ⁇ m and ⁇ (m) we use the notations ⁇ m and ⁇ (m) to denote row or column m of a matrix.
  • the CS theory tells us that when certain conditions hold, namely that the basis cannot sparsely represent the elements of the sparsity-inducing basis (a condition known as incoherence of the two bases) and the number of measurements M is large enough, then it is indeed possible to recover the set of large ⁇ (n) ⁇ (and thus the signal x) from a similarly sized set of measurements ⁇ y(m) ⁇ .
  • This incoherence property holds for many pairs of bases, including for example, delta spikes and the sine waves of the Fourier basis, or the Fourier basis and wavelets.
  • this incoherence also holds with high probability between an arbitrary fixed basis and a randomly generated one (consisting of i.i.d. Gaussian or Bemoulli/Rademacher ⁇ 1 vectors). Signals that are sparsely represented in frames or unions of bases can be recovered from incoherent measurements in the same fashion.
  • the l 0 norm ⁇ 0 counts the nonzero entries in the vector ⁇ ; hence it is a measure of the degree of sparsity, with more sparse vectors having smaller l o norm.
  • the optimization problem (2) also known as Basis Pursuit (see Chen, S., Donoho, D., Saunders, M., “Atomic decomposition by basis pursuit,” SIAM J. on Sci. Comp. 20 (1998) 33-61), is significantly more approachable and can be solved with traditional linear programming techniques whose computational complexities are polynomial in N. Although only K+1 measurements are required to recover sparse signals via l o optimization, one typically requires M ⁇ cK measurements for Basis Pursuit with an overmeasuring factor c>1.
  • any reconstruction approach can be used in the present invention.
  • Other examples include the (potentially more efficient) iterative Orthogonal Matching Pursuit (OMP) (see Tropp, J., Gilbert, A. C., “Signal recovery from partial information via orthogonal matching pursuit,” (2005) Preprint), matching pursuit (MP)(see Mallat, S. and Zhang, Z., “Matching Pursuit with Time Frequency Dictionaries”, (1993) IEEE Trans. Signal Processing 41(12): 3397-3415), tree matching pursuit (TMP) (see Duarte, M. F., Wakin, M. B., Baraniuk, R. G., “Fast reconstruction of piecewise smooth signals from random projections,” Proc.
  • Belief Propagation see Pearl, J., “Fusion, propagation, and structuring in belief networks”, (1986) Artificial Intelligence, 29(3): 241-288
  • LASSO see Tibshirani, R., “Regression shrinkage and selection via the lasso”, (1996) J. Royal. Statist. Soc B., 58(1): 267-288
  • LARS see Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., “Least Angle Regression”, (2004) Ann. Statist.
  • Reconstruction can also be based on other signal models, such as manifolds (see Wakin, M, and Baraniuk, R., “Random Projections of Signal Manifolds” IEEE ICASSP 2006, May 2006, to appear).
  • Manifold models are completely different from sparse or compressible models. Reconstruction algorithms in this case are not necessarily based on sparsity in some basis/frame, yet signals/images can be measured using the systems described here.
  • the systems described here can also be used to acquire a collection of images or video sequences.
  • Each image or video can be viewed as a point in N-dimensional Euclidean space. Therefore, the collection of images/videos forms a point cloud in N dimensional Euclidean space.
  • Incoherent projections as implemented in our systems will keep different images/videos well-separated and preserve the neighborhood relationships among similar signals, even if we never intend to reconstruct these images/videos (see Dasgupta, S., Gupta, A., “An elementary proof of the Johnson-Lindenstrauss lemma,” Tech. Rep. TR-99-006, Berkeley, Calif., 1999).
  • the point cloud approach is useful for posing and solving decision problems with collections of images/videos, such as detection, classification, recognition, tracking, registration, and other problems.
  • the present invention is a new system to support what can be called Compressive Imaging (CI).
  • the present invention incorporates a microcontrolled mirror array driven by pseudorandom and other measurement bases and a single or multiple photodiode optical sensor.
  • This hardware optically computes incoherent image measurements as dictated by the CS theory; CS reconstruction algorithms are then applied to obtain the acquired images.
  • a camera in accordance with the present invention can also be used to take streaming measurements of a video signal, which can then be recovered using CS techniques designed for either 2D frame-by-frame reconstruction or joint 3D reconstruction. Streaming video can also be supported.
  • One possible hardware realization of the CI concept is a single detector camera; it combines a microcontrolled mirror array displaying a time sequence of M pseudorandom basis images with a single optical sensor to compute incoherent image measurements y as in (1) (see FIG. 1 ).
  • the present invention trades off the amount of compression versus acquisition time; in contrast, conventional cameras trade off resolution versus the number of pixel sensors.
  • FIG. 1 shows a compressive imaging (CI) camera in accordance with a preferred embodiment of the present invention.
  • An incident lightfield 110 corresponding to the desired image x passes through a lens 120 and is then reflected off a digital micromirror device (DMD) array 140 whose mirror orientations are modulated in the pseudorandom pattern sequence supplied by the random number generator or generators 130 .
  • DMD digital micromirror device
  • Each different mirror pattern produces a voltage at the single photodiode detector 160 that corresponds to one measurement y(m). While only one photodetector is shown in FIG. 1 , any number of detectors may be used, although typically, the number of photodetectors will be less than the total number of ultimate number of pixels obtained in the image.
  • the voltage level is then quantized by an analog-to-digital converter 170 .
  • the bitstream produced is then communicated to a reconstruction algorithm 180 , which yields the output image 190 .
  • a preferred embodiment of the invention employs a Texas Instruments digital micromirror device (DMD) for generating the random modulation basis patterns.
  • the DMD consists of a 1024 ⁇ 768 array of electrostatically actuated micromirrors where each mirror of the array is suspended above an individual SRAM cell. Each mirror rotates about a hinge and can be positioned in one of two states (+12 degrees and ⁇ 12 degrees from horizontal); thus light falling on the DMD may be reflected in two directions depending on the orientation of the mirrors.
  • the Texas Instruments DMD is one possible embodiment, but many additional embodiments are possible.
  • the desired image is formed on the DMD plane 140 ; this image acts as an object for the second biconvex lens 150 , which focuses the image onto the photodiode 160 .
  • the light is collected from one of the two directions in which it is reflected (e.g., the light reflected by mirrors in the +12 degree state).
  • the light from a given configuration of the DMD mirrors 140 is summed at the photodiode 160 to yield an absolute voltage that yields a coefficient y(m) for that configuration.
  • the output of the photodiode 160 is amplified through an op-amp circuit and then digitized by a 12-bit analog to digital converter 170 .
  • the photodiode measurements can be interpreted as the inner product of the desired image x with a measurement basis vector ⁇ m .
  • ⁇ (m) denote the mirror positions of the m-th measurement pattern
  • the voltage reading from the photodiode v(m) can be written as v ( m ) ⁇ x, ⁇ m T >+DC offset (3)
  • 1 ⁇ . ⁇ is the indicator function.
  • the DC offset can be measured by setting all mirrors to ⁇ 12 degrees; it can then subtracted off.
  • Equation (3) holds the key for implementing a compressive imaging (CI) system.
  • CI compressive imaging
  • random or pseudorandom measurement patterns enjoy a useful universal incoherence property with any fixed basis, and so we employ pseudorandom ⁇ 12 degree patterns on the mirrors. These correspond to pseudorandom 0/1 Bernoulli measurement vectors.
  • the measurements may easily be converted to ⁇ 1 Rademacher patterns by setting all mirrors in ⁇ (1) to +12 degrees and then letting y(m) ⁇ 2y(m) ⁇ y(1) for m>1.
  • Other options for incoherent CI mirror patterns include ⁇ 1/0/1 group-testing patterns (see Cormode, G., Muthukrishnan, S.: Towards an algorithmic theory of compressed sensing. DIMACS Tech. Report 2005-40 (2005)). These are specific embodiments of mirror patterns; additional embodiments of mirror patterns can also be used.
  • Mirrors can also be duty-cycled to give the elements of ⁇ finer precision, for example to approximate Gaussian measurement vectors (see D. Donoho, “Compressed Sensing,” IEEE Transactions on Information Theory, Volume 52, Issue 4, April 2006, Pages: 1289-1306; and Candès, E., Tao, T., “Near optimal signal recovery from random projections and universal encoding strategies,” (2004) Preprint).
  • This duty-cycling technique can be used to emulate inner products with any real-valued vector. Specific embodiments may generate each coefficient of such projection vectors using some continuous probability distribution, but any set of real-valued vector values can be used.
  • This compressive imaging system directly acquires a reduced set of M incoherent projections of an N-pixel image x without first acquiring the N pixel values. Since the camera is “progressive,” better quality images (larger K) can be obtained by taking a larger number of measurements M. Also, since the data measured by the camera is “future-proof,” new reconstruction algorithms based on better sparsifying image transforms can be applied at a later date to obtain even better quality images.
  • the CI system and method of the present invention is immediately applicable to video acquisition.
  • the measurements are taken sequentially in time.
  • the 3D measurement matrix ⁇ enjoys sufficient incoherence with the 3D sparsity matrix ⁇ .
  • the video could also be reconstructed using the manifold-based reconstruction algorithms described above (see Wakin, M, and Baraniuk, R., “Random Projections of Signal Manifolds” IEEE ICASSP 2006, May 2006, to appear).
  • the compressive imaging architecture and method of the present invention can also be extended to acquire full 3D measurements of a video sequence (that is, where each has 3D support).
  • One embodiment of such 3D measurements would combine inner products sampled at different times, but other embodiments are possible.
  • FIG. 2 ( a ) shows the printout.
  • the video shows a disk moving from top to bottom and growing from small to large.
  • This embodiment can be extended by developing better joint reconstruction techniques, perhaps by using algorithms for Distributed CS (see Baron, D., Wakin, M. B., Duarte, M. F., Sarvotham, S., Baraniuk, R. G., “Distributed compressed sensing” (2005)) for video reconstruction.
  • Distributed CS see Baron, D., Wakin, M. B., Duarte, M. F., Sarvotham, S., Baraniuk, R. G., “Distributed compressed sensing” (2005)
  • the optical signal to be acquired 410 runs through the focusing lens 420 which is focused onto the Masking/Modulation Device 430 .
  • This device is configured according to the chosen optical modulation sequence 440 .
  • the reflection of the image on the device performs a pixel-wise multiplication 450 of the values of the optical signal 410 and the optical modulation sequence 440 , and a new optical product signal is obtained.
  • This optical signal is focused by a second lens 460 onto a single optical sensing element 470 , which registers the sum of the absolute values of the entries in the product signal, thus returning the value of the inner product 480 .
  • the adaptive imaging apparatus takes the optical signal to be acquired 510 , and a specified optical modulation sequence 520 , and performs an optical inner product of these two signals 530 to obtain a projection value.
  • the projection value is stored 540 and the apparatus checks whether the number of projections necessary for reconstruction has been reached 550 . If it has not, then the apparatus employs a new specified optical modulation sequence 522 to obtain new projection values. If it has, then the projection values and the modulation sequences 520 are fed into the reconstruction algorithm 560 , which obtains a reconstruction of the optical signal 570 .
  • Many possible embodiments for adapting the number of measurements to the specific signal can be used.
  • Another approach is to monitor instances of change.
  • the received image light field may not change for long periods of time, or the images may change very slowly, allowing a slower data acquisition rate.
  • a camera used to monitor an infrequently used stairwell does not really need to report the same image of the stairwell over and over.
  • the computation determining when the camera should wake up should be as simple as possible, using low-power analog processing and/or low-rate digital processing.
  • the camera needs a startle reflex: a quick reaction mode involving little overhead or computation.
  • the CI camera is ideally suited to implement a startle reflex.
  • the values or statistics of coefficients at the analog-to-digital converter output, running at a very slow, low-power rate would be compared with previous values or statistics to determine whether the scene being monitored has changed.
  • Algorithms for achieving this could range from very simple statistical (parametric and non-parametric) tests, for example a test based on an energy detector, a test based on empirical entropies (see Gutman, M., “Asymptotically Optimal Classification for Multiple Tests with Empirically Observed Statistics,” IEEE Trans. Inform. Theory 35 (1989) 401-408), or more sophisticated tests based on detailed models of motion.
  • the startle-reflex algorithm may be described as follows:
  • Attention based processing is not limited to turning a CI camera on and off. Additional embodiments may use the attention information for additional purposes, for example to track different phenomena in space and/or time.
  • An embodiment of the present invention exploits the incorporation of a microcontrolled mirror (driven by either piezoelectrics or electrostatics) with an optical sensor so that it can additionally acquire images, instead of adapting current camera technology to be employed as an optical sensor.
  • a microcontrolled mirror driven by either piezoelectrics or electrostatics
  • the material below describes such a preferred embodiment, which is an alternative to the embodiment using DMD arrays described above.
  • Photodiode Sensing Element By replacing the optical sensor array with a single sensing element (in this case a photodiode), we have greatly reduced the complexity. In shifting the complexity of the camera away from the signal receiving portion and into the signal acquisition/interpretation areas, we are able to work with less expensive and more sensitive photon detectors.
  • the advantages of a photodiode sensing element include low-power, low cost, high photon sensitivity that increases with the diode volume, and very fast response times. Modern photodiodes are routinely operated at hundreds of megahertz and have been extended into the GHz regime. The cost of photodiodes can be as little as $0.50 a chip with the cost increasing with the performance capabilities; still, a very good quality photodiode is around $15.
  • having one optical receiving element allows us to increase the detection efficiency by increasing its size.
  • the single photodiode can be replaced with a quadrant photodiode for higher spatial resolution.
  • the normal photodiode may be replaced with an avalanche photodiode to allow single photon counting.
  • photodiodes a variety of semiconductor materials are available, allowing the sensitivity to span the optical, UV, and IR spectrums with equal capabilities. While the initial prototype will be was grayscale, the conversion of this device to a full color camera has been straightforward. For proof of concept we have implemented color imaging in our architecture with RGB filters mounted on a color wheel. However, many other color technologies may also be adapted with our imaging architecture.
  • Color image/video reconstruction can be facilitated by the fact that the color channels share common information (they are correlated or “jointly sparse”). Therefore, the techniques of distributed compressed sensing could be used to lower the number of measurements required for color data acquisition (see Baron, D., Wakin, M. B., Duarte, M. F., Sarvotham, S., Baraniuk, R. G., “Distributed compressed sensing” (2005).
  • Photodiodes can be implemented in a variety of circuit configurations depending on the application. Output voltage could be set up to be proportional to logarithmic change in the detected light level, the external circuit could optimized to emphasize the converting the light signal to frequency, or an x-ray scintillation detector could be mounted in front of the photodiode for medical or astronomy applications (with the appropriate modifications to the mirror coating). These are specific embodiments; additional specific embodiments of photodiodes are possible.
  • the Texas Instruments Digital Micromirror Device is composed of an array of electrostatically actuated micromirrors that has found a great deal of success in the projection screen market (see D. Doherty and G. Hewlett, “Phased reset timing for improved digital micromirror device (DMD) brightness,” in SID Symposium Digest , vol. 29, p. 125; L. Hornbeck, “Current status of the digital micromirror device (DMD) for projection television applications,” International Electron Devices Technical Digest , p. 1993, 15.1.1; J.
  • Each mirror 610 , 620 in a two-dimensional (x-y) array of mirrors is suspended above an individual SRAM cell in an x-y array of memory cells on a substrate 660 .
  • Electrostatic forces are created between the mirrors and address electrodes connected to the SRAM nodes at which the “1” or “0” voltages appear. These forces twist the mirrors one way or the other about an axis through the torsion hinges 630 until the rotation is stopped at a precise angle determined by one mirror edge or the other touching the underlying substrate.
  • a small tilting yoke 640 , springs 650 , address electrodes, torsion hinges 630 , and landing electrodes are created to control the mirror tilt (this is shown in FIG. 6 ( a )).
  • a second sacrificial polymer layer is deposited onto this aluminum layer and vias are created from the surface of that layer to the center of each yoke 640 .
  • a square mirror is fabricated integral to the post formed by each via. Two sacrificial layers are removed simultaneously, leaving mirrors that tilt as before (as the yokes they ride on are tilted) but that minimize light diffracted from the underlying structure. An array of such mirrors is shown in FIG. 6 ( b ). This mirror structure has been migrated to the 768 ⁇ 576 pixel DMD, and contrast ratios from both front and rear projection systems based on such mirrors routinely exceed 100:1.
  • any micromirror array either electrostatically or piezoelectrically driven, is suitable for our camera architecture. Similar driving mechanisms may also be suitable for our camera architecture.
  • Piezoelectric Deformable Mirror As an alternative to electrostatic manipulation in MEMs devices, piezoelectric materials offer a similar ability to convert electrical signals into mechanical work. At the same time, they are able to actuate at much greater frequencies compared to electrostatic based systems. A piezoelectric transducer can reach its nominal displacement in 1 ⁇ 3 of the period of the resonant frequency. Depending of the final size and scale of the tranducers, these frequencies could correspond to a time on the order of microseconds. Electrostatic structures are still favored in MEMs applications due to the ease of the incorporation with traditional IC manufacturing technologies.
  • the protrusions in the switching mirror 710 would act as incoherent scatters and should only shift the overall background while the main contribution to the encoded signal on the photodiode comes from the unperturbed mirror pixels.
  • the angle of reflection between the lenses 730, 740 and mirror must not be too shallow, otherwise the undeformed neighboring pixels on the mirror might be shadowed by their protruding neighbors.
  • the second, off-center configuration of the switching mirror illustrated in FIG. 7 ( c ) would attempt to increase the contrast ratio by reflecting the light from the mirror into the photodiode at a more oblique angle.
  • the switching mirror bears more of a resemblance to a tunable diffraction grating (see C. W. Wong, Y. Jeon, G. Barbastathis, and S. G. Kim, “Analog tunable gratings driven by thin-film piezoelectric microelectromechanical actuators,” Applied Optics , vol. 42, pp. 621-626, 2003). After various modeling and testing, the most appropriate device structure will be adopted.
  • Another possible embodiment includes a microcontroller that drives the mirror motions in such a manner that the mirror surface structure forms a time-varying 2D smooth surface. Controlling the mirror structure to conform to the desired smooth surface will enable the mirror angle to vary smoothly between spatially close-by locations on the mirror surface. Therefore, in contrast to the measurement via discretely-computed inner products mentioned before, in this system the device will sense the continuous integral of the optical signal modulated by the mirror surface waveform. This capability will enable advanced analog measurement techniques. This integral can be written as an inner product not between two discrete, length-N vectors but between two continuously varying 2D functions.
  • Another possible embodiment is to perform image acquisition using real-space convolution with white-noise as a shuttering mechanism and recorded by an individual detector.
  • an image may be formed using a similar modulation of a (potentially micro-electromechanical) shutter array placed directly over the detector. This would create an essentially flat camera. In fact, the modulation mechanism of our image signal in transmission mode would apply well beyond the optical regime allowing for construction of a camera out of a single sensor in regimes where reflective optical elements do not exist, such as gamma rays.
  • Compressive imaging can be incorporated in distributed systems with multiple imaging devices. This will enable reconstruction of multiple images (e.g., multiple frames of a video sequence) using fewer measurements than before, requiring reduced resource consumption (e.g. power reduction). Alternatively, these techniques could enable better image reconstruction quality.
  • Power reduction can be achieved by minimizing the amount of mirror motion.
  • One way to do this is to specifically design measurement matrices such that adjacent rows are as similar as possible to one another.

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Cited By (106)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080197842A1 (en) * 2007-02-13 2008-08-21 The Board Of Trustees Of The Leland Stanford Junior University K-t sparse: high frame-rate dynamic magnetic resonance imaging exploiting spatio-temporal sparsity
US20080212424A1 (en) * 2007-03-01 2008-09-04 Lite-On It Corp. Data modulation/encryption method used in holographic storage system
US20080225688A1 (en) * 2007-03-14 2008-09-18 Kowalski John M Systems and methods for improving reference signals for spatially multiplexed cellular systems
US20080250875A1 (en) * 2007-04-13 2008-10-16 Raytheon Company Sparse sampling planner for sensor resource management
US20080310383A1 (en) * 2007-06-15 2008-12-18 Sharp Laboratories Of America, Inc. Systems and methods for designing a sequence for code modulation of data and channel estimation
US20090110034A1 (en) * 2007-10-30 2009-04-30 Sharp Laboratories Of America, Inc. Systems and methods for generating sequences that are nearest to a set of sequences with minimum average cross-correlation
US20090196513A1 (en) * 2008-02-05 2009-08-06 Futurewei Technologies, Inc. Compressive Sampling for Multimedia Coding
WO2009115961A1 (fr) 2008-03-18 2009-09-24 Koninklijke Philips Electronics N. V. Caméra d'étalonnage à profondeur spectrale
US20090322870A1 (en) * 2008-06-30 2009-12-31 Olympus Corporation Microscope imaging apparatus and its system
US20100053352A1 (en) * 2008-09-02 2010-03-04 General Electric Company Methods and apparatus to capture compressed images
US20100077016A1 (en) * 2008-09-24 2010-03-25 Eduardo Perez Estimating a Signal Based on Samples Derived from Random Projections
US20100080473A1 (en) * 2008-09-26 2010-04-01 Futurewei Technologies, Inc. System and Method for Compressing and Decompressing Images and Video
US20100194926A1 (en) * 2009-01-30 2010-08-05 Kang Joo-Young Apparatus and method for acquiring light field data using variable modulator
US20100241378A1 (en) * 2009-03-19 2010-09-23 Baraniuk Richard G Method and Apparatus for Compressive Parameter Estimation and Tracking
US7834795B1 (en) 2009-05-28 2010-11-16 Bae Systems Information And Electronic Systems Integration Inc. Compressive sensor array system and method
US20110007795A1 (en) * 2009-07-13 2011-01-13 L3 Communications Intergrated Systems, L.P. Compressed sensing characterization system and method
WO2011071958A2 (fr) * 2009-12-07 2011-06-16 William Marsh Rice University Appareil et procédé d'imagerie compressive et de détection par modulation multiplexée
US20110142339A1 (en) * 2009-11-20 2011-06-16 Tripurari Singh Method and System for Compressive Color Image Sampling and Reconstruction
WO2011103601A2 (fr) * 2010-02-22 2011-08-25 William Marsh Rice University Nombre augmenté de pixels dans des matrices de détecteurs à l'aide d'une détection de compression
US20120016921A1 (en) * 2009-03-19 2012-01-19 Mark A Davenport Method and Apparatus for Compressive Domain Filtering and Interference Cancellation
US8112041B2 (en) 2007-03-14 2012-02-07 Sharp Kabushiki Kaisha Systems and methods for generating sequences that are nearest to a set of sequences with minimum average cross-correlation
WO2012021450A1 (fr) 2010-08-11 2012-02-16 Inview Technology Corporation Diminution du temps d'acquisition d'images pour des dispositifs de formation d'images à compression
US20120038805A1 (en) * 2010-08-11 2012-02-16 Kelly Kevin F Dynamic Range Optimization and Hot Spot Correction in a Compressive Imaging System
WO2012044468A1 (fr) * 2010-09-30 2012-04-05 Alcatel Lucent Appareil et procédé pour générer des mesures de compression d'une vidéo utilisant une intégration spatiale et temporelle
US20120082393A1 (en) * 2010-09-30 2012-04-05 The Charles Stark Draper Laboratory, Inc. Attitude estimation with compressive sampling of starfield data
WO2012044380A2 (fr) * 2010-06-18 2012-04-05 William Marsh Rice University Procédé et appareil pour une acquisition compressive et une récupération d'images dynamiques
US20120127184A1 (en) * 2010-11-19 2012-05-24 Ricoh Company, Ltd. Image projection apparatus, memory control apparatus, laser projector, and memory access method
US20120188433A1 (en) * 2011-01-20 2012-07-26 Canon Kabushiki Kaisha Apparatus and method for compressive sampling
WO2012146156A1 (fr) * 2011-04-25 2012-11-01 中国科学院空间科学与应用研究中心 Système d'imagerie par comptage monophotonique et procédé associé
WO2012174940A1 (fr) * 2011-06-20 2012-12-27 中国科学院空间科学与应用研究中心 Procédé d'imagerie multispectrale pour émission photonique ultrafaible et système associé
WO2013003485A1 (fr) * 2011-06-28 2013-01-03 Inview Technology Corporation Reconstruction de séquence d'images s'appuyant sur des sous-ensembles de mesures chevauchants
WO2013066896A1 (fr) * 2011-10-31 2013-05-10 Inview Technology Corporation Détection rapide d'évènement à l'aide d'une architecture d'imagerie hyperstructurale à acquisition comprimée
CN103115681A (zh) * 2013-01-24 2013-05-22 中国科学院空间科学与应用研究中心 一种超灵敏时间分辨光谱仪及其时间分辨方法
CN103115680A (zh) * 2013-01-24 2013-05-22 中国科学院空间科学与应用研究中心 一种超灵敏光谱仪以及光谱检测方法
US20130128042A1 (en) * 2011-06-28 2013-05-23 InView Techonogogy Corporation High-speed event detection using a compressive-sensing hyperspectral-imaging architecture
US8472737B2 (en) 2010-09-30 2013-06-25 The Charles Stark Draper Laboratory, Inc. Attitude estimation in compressed domain
US8472736B2 (en) 2010-09-30 2013-06-25 The Charles Stark Draper Laboratory, Inc. Attitude estimation by reducing noise with dragback
US20130335546A1 (en) * 2012-06-18 2013-12-19 Randall T. Crane Selective imaging
US20140037215A1 (en) * 2012-08-03 2014-02-06 Mrityunjay Kumar Identifying key frames using group sparsity analysis
US20140036041A1 (en) * 2012-08-03 2014-02-06 Leung CHI WAI Digital camera, laminated photo printer and system for making 3d color pictures
US20140063314A1 (en) * 2012-02-28 2014-03-06 Aswin C Sankaranarayanan System And Method Of Video Compressive Sensing For Spatial-Multiplexing Cameras
CN103630240A (zh) * 2013-11-14 2014-03-12 中国科学院上海光学精密机械研究所 物体表面颜色测量装置与方法
US8717463B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation Adaptively filtering compressive imaging measurements to attenuate noise
US8717484B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation TIR prism to separate incident light and modulated light in compressive imaging device
US8760542B2 (en) * 2010-08-11 2014-06-24 Inview Technology Corporation Compensation of compressive imaging measurements based on measurements from power meter
US20140198236A1 (en) * 2013-01-16 2014-07-17 Inview Technology Corporation Generating Modulation Patterns for the Acquisition of Multiscale Information in Received Signals
CN103968945A (zh) * 2014-05-28 2014-08-06 中国科学院空间科学与应用研究中心 基于二阶压缩感知的超灵敏光谱成像天文望远镜及方法
CN104019899A (zh) * 2014-05-28 2014-09-03 中国科学院空间科学与应用研究中心 一种超灵敏天文望远镜及其天文图像获取方法
US20140253713A1 (en) * 2011-10-25 2014-09-11 Guangjie Zhai Time-Resolved Single-Photon or Ultra-Weak Light Multi-Dimensional Imaging Spectrum System and Method
WO2014144306A1 (fr) * 2013-03-15 2014-09-18 Arizona Board Of Regents On Behalf Of Arizona State University Modèles clairsemés d'ensemble pour analyse et restauration d'image
WO2014155026A1 (fr) * 2013-03-27 2014-10-02 Toshiba Research Europe Limited Procédé et appareil de traitement de signal
US8907280B1 (en) 2012-09-19 2014-12-09 Sandia Corporation Fast electron microscopy via compressive sensing
US20150003750A1 (en) * 2013-07-01 2015-01-01 Xerox Corporation Reconstructing an image of a scene captured using a compressed sensing device
US8929456B2 (en) 2010-09-30 2015-01-06 Alcatel Lucent Video coding using compressive measurements
US20150023608A1 (en) * 2004-08-09 2015-01-22 David Leigh Donoho Method and apparatus for compressed sensing
CN104320567A (zh) * 2014-10-29 2015-01-28 中国科学院半导体研究所 数字微镜阵列编码闪光三维成像方法及装置
US20150036021A1 (en) * 2011-11-10 2015-02-05 Centre National De La Recherche Scientifique - Cnrs Multiple Scattering Medium For Compressive Imaging
US8958005B1 (en) * 2012-08-30 2015-02-17 Google Inc. Single pixel camera
CN104702828A (zh) * 2015-03-13 2015-06-10 四川大学 一种分块阵列块内压缩感知逐点扫描相机
US9092890B2 (en) 2012-12-20 2015-07-28 Ricoh Company, Ltd. Occlusion-aware reconstruction of three-dimensional scenes from light field images
US20150215529A1 (en) * 2014-01-24 2015-07-30 Goodrich Corporation Random imaging
US20150234443A1 (en) * 2014-02-20 2015-08-20 West Virginia University Selective wakeup of digital sensing and processing systems using reconfigurable analog circuits
CN105205496A (zh) * 2015-09-11 2015-12-30 重庆邮电大学 基于空间信息约束的增强型稀疏表示高光谱图像分类装置及方法
US20160006916A1 (en) * 2012-02-07 2016-01-07 Alcatel-Lucent Usa Inc. Lensless compressive image acquisition
US20160048950A1 (en) * 2014-05-16 2016-02-18 North Carolina State University Compressive imaging using approximate message passing with denoising
WO2016028200A1 (fr) * 2014-08-21 2016-02-25 Totalförsvarets Forskningsinstitut Système d'imagerie de parallélisation d'imagerie par acquisition comprimée
US20160065824A1 (en) * 2014-08-26 2016-03-03 Sensors Unlimited, Inc. High dynamic range image sensor
US9319578B2 (en) 2012-10-24 2016-04-19 Alcatel Lucent Resolution and focus enhancement
US9344736B2 (en) 2010-09-30 2016-05-17 Alcatel Lucent Systems and methods for compressive sense imaging
US9398310B2 (en) 2011-07-14 2016-07-19 Alcatel Lucent Method and apparatus for super-resolution video coding using compressive sampling measurements
US9398229B2 (en) 2012-06-18 2016-07-19 Microsoft Technology Licensing, Llc Selective illumination of a region within a field of view
US20160276050A1 (en) * 2015-03-18 2016-09-22 Battelle Memorial Institute Electron beam masks for compressive sensors
US9477829B2 (en) * 2011-03-16 2016-10-25 Apple Inc. Locking and unlocking a mobile device using facial recognition
US9518916B1 (en) * 2013-10-18 2016-12-13 Kla-Tencor Corporation Compressive sensing for metrology
US20170025247A1 (en) * 2015-03-18 2017-01-26 Battelle Memorial Institute Tem phase contrast imaging with image plane phase grating
US9563806B2 (en) 2013-12-20 2017-02-07 Alcatel Lucent Methods and apparatuses for detecting anomalies using transform based compressed sensing matrices
US20170041166A1 (en) * 2015-08-05 2017-02-09 National Taiwan University Compressive sensing system based on personalized basis and method thereof
US9600899B2 (en) 2013-12-20 2017-03-21 Alcatel Lucent Methods and apparatuses for detecting anomalies in the compressed sensing domain
US9615022B2 (en) 2014-12-11 2017-04-04 Conduent Business Services, Llc High-resolution imaging devices using low-resolution sensors and compressive sensing exploiting joint sparsity
US9634690B2 (en) 2010-09-30 2017-04-25 Alcatel Lucent Method and apparatus for arbitrary resolution video coding using compressive sampling measurements
US9719940B2 (en) * 2014-03-20 2017-08-01 KLA—Tencor Corporation Compressive sensing with illumination patterning
US9759995B2 (en) 2011-08-18 2017-09-12 Massachusetts Institute Of Technology System and method for diffuse imaging with time-varying illumination intensity
US20170357000A1 (en) * 2016-06-09 2017-12-14 Texas Instruments Incorporated Processing techniques for lidar receiver using spatial light modulators
US9894324B2 (en) 2014-07-15 2018-02-13 Alcatel-Lucent Usa Inc. Method and system for modifying compressive sensing block sizes for video monitoring using distance information
CN107796788A (zh) * 2016-08-29 2018-03-13 南京理工大学 基于变分贝叶斯期望最大算法的传感矩阵测量方法
EP3161520A4 (fr) * 2014-06-27 2018-03-14 HRL Laboratories, LLC Lidar à balayage compressif
US9990699B1 (en) * 2016-03-21 2018-06-05 Rockwell Collins, Inc. Block match denoising for real-time imaging
WO2018134729A1 (fr) * 2017-01-18 2018-07-26 Technion Research & Development Foundation Ltd. Imagerie à super-résolution ultrasonore basée sur la rareté
US10088753B1 (en) * 2017-05-17 2018-10-02 Powerchip Technology Corporation Reconfiguring image brightness module and related method
US10136116B2 (en) 2016-03-07 2018-11-20 Ricoh Company, Ltd. Object segmentation from light field data
US10140249B2 (en) 2015-06-05 2018-11-27 North Carolina State University Approximate message passing with universal denoising
US20190147589A1 (en) * 2017-11-10 2019-05-16 Shenzhen United Imaging Healthcare Co., Ltd. System and method for image reconstruction
US10295677B2 (en) 2017-05-08 2019-05-21 Battelle Memorial Institute Systems and methods for data storage and retrieval
CN110166678A (zh) * 2019-06-26 2019-08-23 京东方科技集团股份有限公司 图像采集结构及其采集方法、显示装置
US10502990B2 (en) 2015-06-22 2019-12-10 Samsung Display Co., Ltd. Liquid crystal display comprising first, second, and third pixels having driving voltages for maximum transmittances that are different from each other
CN110632000A (zh) * 2019-09-06 2019-12-31 中国科学院西安光学精密机械研究所 基于压缩感知的动态双臂多通道凝视光谱成像系统
WO2020020805A1 (fr) 2018-07-23 2020-01-30 Politecnico Di Milano Dispositif et procédé de détection de données optiques à résolution temporelle
US10580614B2 (en) 2016-04-29 2020-03-03 Battelle Memorial Institute Compressive scanning spectroscopy
US10616464B2 (en) 2013-02-15 2020-04-07 Apple Inc. Apparatus and method for automatically activating a camera application based on detecting an intent to capture a photograph or a video
US10616520B2 (en) * 2015-07-01 2020-04-07 Massachusetts Institute Of Technology Method and apparatus for on-chip per-pixel pseudo-random time coded exposure
CN111226141A (zh) * 2017-10-20 2020-06-02 澳大利亚核科学和技术组织 压缩成像方法和系统
CN111640069A (zh) * 2020-04-17 2020-09-08 上海交通大学 基于光感知网络和相位补偿的压缩成像方法、系统和装置
CN113050872A (zh) * 2019-12-26 2021-06-29 财团法人工业技术研究院 传感器上的数据处理系统及其方法与去识别化传感装置
CN113890801A (zh) * 2021-12-08 2022-01-04 浙大城市学院 采用频域编码混频的压缩感知信号获取系统及方法
US20220417407A1 (en) * 2019-12-25 2022-12-29 Hamamatsu Photonics K.K. Imaging device and imaging method
US11852795B2 (en) 2019-12-25 2023-12-26 Hamamatsu Photonics K.K. Imaging device and imaging method

Families Citing this family (62)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10213164B2 (en) * 2008-09-26 2019-02-26 Qualcomm Incorporated Method and apparatus for under-sampled acquisition and transmission of photoplethysmograph (PPG) data and reconstruction of full band PPG data at the receiver
US9293197B2 (en) * 2011-08-15 2016-03-22 Lockheed Martin Corporation Reconfigurable phase change material masks for electro-optical compressive sensing
US8953012B2 (en) 2012-02-13 2015-02-10 Raytheon Company Multi-plenoptic system with image stacking and method for wide field-of-regard high-resolution imaging
US8976299B2 (en) * 2012-03-07 2015-03-10 Intellectual Ventures Fund 83 Llc Scene boundary determination using sparsity-based model
US8982958B2 (en) * 2012-03-07 2015-03-17 Intellectual Ventures Fund 83 Llc Video representation using a sparsity-based model
US9613285B2 (en) 2012-03-22 2017-04-04 The Charles Stark Draper Laboratory, Inc. Compressive sensing with local geometric features
US8983198B2 (en) 2012-03-22 2015-03-17 The Charles Stark Draper Laboratory, Inc. Compressive sensing with local geometric features
US20140211000A1 (en) * 2013-01-31 2014-07-31 Inview Technology Corporation Sensing Signals with Affine-Harmonically Related Rows of Kronecker-Product Matrices
US9340446B1 (en) 2013-02-04 2016-05-17 Lockheed Martin Corporation Optical apparatus and method of forming a gradient index device
US9124808B2 (en) 2013-03-04 2015-09-01 Raytheon Company Foveated imaging system and method
US9188785B2 (en) 2013-03-15 2015-11-17 Xerox Corporation Single-pixel camera architecture with simultaneous multi-band acquisition
US10113870B2 (en) 2013-03-20 2018-10-30 Cognex Corporation Machine vision system for forming a digital representation of a low information content scene
US9453730B2 (en) 2013-03-20 2016-09-27 Cognex Corporation Machine vision 3D line scan image acquisition and processing
US9838645B2 (en) 2013-10-31 2017-12-05 Elwha Llc Remote monitoring of telemedicine device
US9075906B2 (en) 2013-06-28 2015-07-07 Elwha Llc Medical support system including medical equipment case
US10036667B2 (en) 2013-07-14 2018-07-31 B. G. Negev Technologies And Applications Ltd., At Ben-Gurion University Thin-layered devices in compressive sensing spectroscopy
US9681051B2 (en) 2013-08-19 2017-06-13 Massachusetts Institute Of Technology Method and apparatus for motion coded imaging
WO2015103566A2 (fr) * 2014-01-06 2015-07-09 The Regents Of The University Of California Imagerie de domaine de fréquence spatial utilisant des modèles personnalisés
US10244223B2 (en) 2014-01-10 2019-03-26 Ostendo Technologies, Inc. Methods for full parallax compressed light field 3D imaging systems
US9253420B2 (en) 2014-02-12 2016-02-02 Xerox Corporation Hyperspectral single pixel imager with fabry perot filter
US9285545B2 (en) 2014-02-21 2016-03-15 Southwest Research Institute Compressive sensing imaging system
CN103955914A (zh) * 2014-02-27 2014-07-30 西安电子科技大学 基于随机投影和Signature/EMD框架的SAR图像分割方法
US9219867B2 (en) 2014-03-27 2015-12-22 Xerox Corporation Method and apparatus for compressive imaging of a scene using a single pixel camera
WO2015154033A1 (fr) * 2014-04-04 2015-10-08 The Arizona Board Of Regents On Behalf Of The University Of Arizona Systèmes de détection par compression et procédés associés
US10091440B1 (en) * 2014-05-05 2018-10-02 Lockheed Martin Corporation System and method for providing compressive infrared imaging
MX363049B (es) 2014-05-22 2019-03-06 Australian Nuclear Science & Tech Org Formacion de imagenes por rayos gamma.
US9823350B2 (en) 2014-07-31 2017-11-21 Raytheon Company Linear mode computational sensing LADAR
US9905233B1 (en) 2014-08-07 2018-02-27 Digimarc Corporation Methods and apparatus for facilitating ambient content recognition using digital watermarks, and related arrangements
US10437083B1 (en) 2014-10-20 2019-10-08 Lockheed Martin Corporation Individually addressable infrared mask array
CN107205780B (zh) * 2015-02-13 2020-09-29 圣犹达医疗用品国际控股有限公司 基于跟踪的3d模型增强
CN107431797B (zh) 2015-04-23 2019-10-11 奥斯坦多科技公司 用于全视差光场显示系统的方法和装置
KR20170140187A (ko) 2015-04-23 2017-12-20 오스텐도 테크놀로지스 인코포레이티드 깊이 정보를 이용한 완전 시차 압축 광 필드 합성을 위한 방법
US10313765B2 (en) 2015-09-04 2019-06-04 At&T Intellectual Property I, L.P. Selective communication of a vector graphics format version of a video content item
KR102395779B1 (ko) 2015-09-23 2022-05-09 삼성전자주식회사 압축 이미징 기술을 이용한 보안장치
US9615024B1 (en) 2015-11-16 2017-04-04 Alcatel-Lucent Usa Inc. Multi-resolution compressive sensing image processing
US10448030B2 (en) 2015-11-16 2019-10-15 Ostendo Technologies, Inc. Content adaptive light field compression
EP3378040A4 (fr) 2015-11-20 2020-01-22 Integrated Dynamic Electron Solutions, Inc. Systèmes de détection de compression temporelle
US9883121B2 (en) 2016-01-15 2018-01-30 Cognex Corporation Machine vision system for forming a one dimensional digital representation of a low information content scene
US10296776B2 (en) 2016-03-31 2019-05-21 Samsung Electronics Co., Ltd. Device and method for biometrics authentication
US10453431B2 (en) 2016-04-28 2019-10-22 Ostendo Technologies, Inc. Integrated near-far light field display systems
US9969647B2 (en) 2016-05-17 2018-05-15 Lockheed Martin Energy, Llc Glass composites having a gradient index of refraction and methods for production thereof
US9908808B1 (en) 2016-08-18 2018-03-06 Lockheed Martin Corporation Ternary glass materials with low refractive index variability
US10270947B2 (en) 2016-09-15 2019-04-23 Microsoft Technology Licensing, Llc Flat digital image sensor
US9652692B1 (en) 2016-09-26 2017-05-16 Cognex Corporation Machine vision system for capturing a digital image of a sparsely illuminated scene
US10616453B2 (en) * 2017-01-11 2020-04-07 Nokia Technologies Oy Audio and visual system including a mask functioning for a camera module and an audio transducer module
US10387751B2 (en) 2017-01-12 2019-08-20 Arizona Board Of Regents On Behalf Of Arizona State University Methods, apparatuses, and systems for reconstruction-free image recognition from compressive sensors
US11051039B2 (en) 2017-06-02 2021-06-29 Ostendo Technologies, Inc. Methods for full parallax light field compression
US20180350038A1 (en) 2017-06-02 2018-12-06 Ostendo Technologies, Inc. Methods and Systems for Light Field Compression With Residuals
TWI655522B (zh) * 2017-08-08 2019-04-01 國立臺灣師範大學 結構光照明數位全像之方法與裝置
CN110999301B (zh) 2017-08-15 2023-03-28 杜比实验室特许公司 位深度高效图像处理
US10432944B2 (en) 2017-08-23 2019-10-01 Avalon Holographics Inc. Layered scene decomposition CODEC system and methods
WO2019148024A1 (fr) 2018-01-26 2019-08-01 Park Jong Kang Systèmes et procédés permettant de réduire la diffusion dans la microscopie multiphotonique à focalisation temporelle
US10931956B2 (en) 2018-04-12 2021-02-23 Ostendo Technologies, Inc. Methods for MR-DIBR disparity map merging and disparity threshold determination
US11172222B2 (en) 2018-06-26 2021-11-09 Ostendo Technologies, Inc. Random access in encoded full parallax light field images
CN109213037B (zh) * 2018-08-28 2021-03-26 南昌大学 采样时间自适应单光子压缩成像控制方法及控制装置
CN109194959B (zh) * 2018-09-28 2021-02-09 中国科学院长春光学精密机械与物理研究所 一种压缩感知成像方法、装置、设备、系统及存储介质
CN109361833B (zh) * 2018-10-08 2020-08-11 南昌大学 一种单光子压缩视频传输装置的传输方法
CN109828285B (zh) * 2019-01-07 2020-10-09 北京理工大学 一种双波段时域压缩感知高速成像方法及装置
WO2020146341A1 (fr) * 2019-01-07 2020-07-16 Futurewei Technologies, Inc. Structure de flux binaire en nuage de points et codage différentiel d'informations auxiliaires
CN111385980B (zh) * 2020-05-19 2022-06-14 桂林智慧产业园有限公司 一种基于粒子群的pcb板贴片方法
CN113884234B (zh) * 2021-09-06 2023-06-09 中国科学院合肥物质科学研究院 一种互补单像素质心探测系统及方法
WO2023084401A1 (fr) * 2021-11-10 2023-05-19 King Abdullah University Of Science And Technology Appareil et procédés d'imagerie hyperspectrale

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4894662A (en) * 1982-03-01 1990-01-16 Western Atlas International, Inc. Method and system for determining position on a moving platform, such as a ship, using signals from GPS satellites
US5412755A (en) * 1991-11-26 1995-05-02 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Optical implementation of inner product neural associative memory
US5465321A (en) * 1993-04-07 1995-11-07 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Hidden markov models for fault detection in dynamic systems
US5546128A (en) * 1991-08-20 1996-08-13 Mitsubishi Denki Kabushiki Kaisha Exposure control apparatus including a spatial light modulator
US5859427A (en) * 1996-06-21 1999-01-12 Asahi Kogaku Kogyo Kabushiki Kaisha Image scanning device
US6273571B1 (en) * 1995-05-23 2001-08-14 Colorlink, Inc. Display architectures using an electronically controlled optical retarder stack
US6313865B1 (en) * 1997-05-08 2001-11-06 Be Here Corporation Method and apparatus for implementing a panoptic camera system
US20020101546A1 (en) * 1995-05-23 2002-08-01 Colorlink, Inc. Color filters, sequencers and displays using color selective light modulators
US6464363B1 (en) * 1999-03-17 2002-10-15 Olympus Optical Co., Ltd. Variable mirror, optical apparatus and decentered optical system which include variable mirror, variable-optical characteristic optical element or combination thereof
US6714585B1 (en) * 1999-06-25 2004-03-30 Ericsson Inc. Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics
US6819469B1 (en) * 2003-05-05 2004-11-16 Igor M. Koba High-resolution spatial light modulator for 3-dimensional holographic display
US20040263989A1 (en) * 2003-06-25 2004-12-30 Eastman Kodak Company Display apparatus
US20060029279A1 (en) * 2004-08-09 2006-02-09 Donoho David L Method and apparatus for compressed sensing

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5895427A (en) * 1989-07-06 1999-04-20 Sulzer Spine-Tech Inc. Method for spinal fixation
US20040001149A1 (en) * 2002-06-28 2004-01-01 Smith Steven Winn Dual-mode surveillance system
US6775049B1 (en) * 2003-01-20 2004-08-10 Texas Instruments Incorporated Optical digital signal processing system and method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4894662A (en) * 1982-03-01 1990-01-16 Western Atlas International, Inc. Method and system for determining position on a moving platform, such as a ship, using signals from GPS satellites
US5546128A (en) * 1991-08-20 1996-08-13 Mitsubishi Denki Kabushiki Kaisha Exposure control apparatus including a spatial light modulator
US5412755A (en) * 1991-11-26 1995-05-02 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Optical implementation of inner product neural associative memory
US5465321A (en) * 1993-04-07 1995-11-07 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Hidden markov models for fault detection in dynamic systems
US20020101546A1 (en) * 1995-05-23 2002-08-01 Colorlink, Inc. Color filters, sequencers and displays using color selective light modulators
US6273571B1 (en) * 1995-05-23 2001-08-14 Colorlink, Inc. Display architectures using an electronically controlled optical retarder stack
US5859427A (en) * 1996-06-21 1999-01-12 Asahi Kogaku Kogyo Kabushiki Kaisha Image scanning device
US6313865B1 (en) * 1997-05-08 2001-11-06 Be Here Corporation Method and apparatus for implementing a panoptic camera system
US6464363B1 (en) * 1999-03-17 2002-10-15 Olympus Optical Co., Ltd. Variable mirror, optical apparatus and decentered optical system which include variable mirror, variable-optical characteristic optical element or combination thereof
US6714585B1 (en) * 1999-06-25 2004-03-30 Ericsson Inc. Rake combining methods and apparatus using weighting factors derived from knowledge of spreading spectrum signal characteristics
US6819469B1 (en) * 2003-05-05 2004-11-16 Igor M. Koba High-resolution spatial light modulator for 3-dimensional holographic display
US20040263989A1 (en) * 2003-06-25 2004-12-30 Eastman Kodak Company Display apparatus
US20060029279A1 (en) * 2004-08-09 2006-02-09 Donoho David L Method and apparatus for compressed sensing

Cited By (205)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150023608A1 (en) * 2004-08-09 2015-01-22 David Leigh Donoho Method and apparatus for compressed sensing
US9626560B2 (en) * 2004-08-09 2017-04-18 The Board Of Trustees Of The Leland Stanford Junior University Method and apparatus for compressed sensing
US20080197842A1 (en) * 2007-02-13 2008-08-21 The Board Of Trustees Of The Leland Stanford Junior University K-t sparse: high frame-rate dynamic magnetic resonance imaging exploiting spatio-temporal sparsity
US7602183B2 (en) 2007-02-13 2009-10-13 The Board Of Trustees Of The Leland Stanford Junior University K-T sparse: high frame-rate dynamic magnetic resonance imaging exploiting spatio-temporal sparsity
US20080212424A1 (en) * 2007-03-01 2008-09-04 Lite-On It Corp. Data modulation/encryption method used in holographic storage system
US8005302B2 (en) * 2007-03-01 2011-08-23 Lite-On It Corp. Data modulation/encryption method used in holographic storage system
US20080225688A1 (en) * 2007-03-14 2008-09-18 Kowalski John M Systems and methods for improving reference signals for spatially multiplexed cellular systems
US20100177834A1 (en) * 2007-03-14 2010-07-15 Sharp Kabushiki Kaisha Systems and methods for improving reference signals for spatially multiplexed cellular systems
US8112041B2 (en) 2007-03-14 2012-02-07 Sharp Kabushiki Kaisha Systems and methods for generating sequences that are nearest to a set of sequences with minimum average cross-correlation
US8116691B2 (en) 2007-03-14 2012-02-14 Sharp Kabushiki Kaisha Systems and methods for improving reference signals for spatially multiplexed cellular systems
WO2008127277A1 (fr) * 2007-04-13 2008-10-23 Raytheon Company Système de planification fondé sur un échantillonnage clairsemé pour gestion de ressources effectuée par des capteurs
US7792598B2 (en) 2007-04-13 2010-09-07 Raytheon Company Sparse sampling planner for sensor resource management
US20080250875A1 (en) * 2007-04-13 2008-10-16 Raytheon Company Sparse sampling planner for sensor resource management
US8428178B2 (en) 2007-06-15 2013-04-23 Sharp Kabushiki Kaisha Systems and methods for designing a sequence for code modulation of data and channel estimation
US20080310383A1 (en) * 2007-06-15 2008-12-18 Sharp Laboratories Of America, Inc. Systems and methods for designing a sequence for code modulation of data and channel estimation
US20100172439A1 (en) * 2007-06-15 2010-07-08 Kowalski John M Systems and methods for designing a sequence for code modulation of data and channel estimation
US20090110034A1 (en) * 2007-10-30 2009-04-30 Sharp Laboratories Of America, Inc. Systems and methods for generating sequences that are nearest to a set of sequences with minimum average cross-correlation
US8611440B2 (en) 2007-10-30 2013-12-17 Huawei Technologies Co., Ltd. Systems and methods for generating sequences that are nearest to a set of sequences with minimum average cross-correlation
US8553994B2 (en) * 2008-02-05 2013-10-08 Futurewei Technologies, Inc. Compressive sampling for multimedia coding
US20090196513A1 (en) * 2008-02-05 2009-08-06 Futurewei Technologies, Inc. Compressive Sampling for Multimedia Coding
US8190007B2 (en) 2008-03-18 2012-05-29 Koninklijke Philips Electronics N.V. Calibration camera with spectral depth
US20110012515A1 (en) * 2008-03-18 2011-01-20 Koninklijke Philips Electronics N.V. Calibration camera with spectral depth
WO2009115961A1 (fr) 2008-03-18 2009-09-24 Koninklijke Philips Electronics N. V. Caméra d'étalonnage à profondeur spectrale
JP2011519430A (ja) * 2008-03-18 2011-07-07 コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ スペクトル深さに関する較正カメラ
US20090322870A1 (en) * 2008-06-30 2009-12-31 Olympus Corporation Microscope imaging apparatus and its system
US20100053352A1 (en) * 2008-09-02 2010-03-04 General Electric Company Methods and apparatus to capture compressed images
US8125549B2 (en) * 2008-09-02 2012-02-28 General Electric Company Methods and apparatus to capture compressed images
US20100077016A1 (en) * 2008-09-24 2010-03-25 Eduardo Perez Estimating a Signal Based on Samples Derived from Random Projections
US8239436B2 (en) 2008-09-24 2012-08-07 National Instruments Corporation Estimating a signal based on samples derived from dot products and random projections
US8805106B2 (en) 2008-09-26 2014-08-12 Futurewei Technologies, Inc. System and method for compressing and decompressing images and video
US20100080473A1 (en) * 2008-09-26 2010-04-01 Futurewei Technologies, Inc. System and Method for Compressing and Decompressing Images and Video
US9204169B2 (en) 2008-09-26 2015-12-01 Futurewei Technologies, Inc. System and method for compressing images and video
US20100194926A1 (en) * 2009-01-30 2010-08-05 Kang Joo-Young Apparatus and method for acquiring light field data using variable modulator
US8648955B2 (en) 2009-01-30 2014-02-11 Samsung Electronics Co., Ltd. Apparatus and method for acquiring light field data using variable modulator
US20100241378A1 (en) * 2009-03-19 2010-09-23 Baraniuk Richard G Method and Apparatus for Compressive Parameter Estimation and Tracking
US8725784B2 (en) * 2009-03-19 2014-05-13 William Marsh Rice University Method and apparatus for compressive domain filtering and interference cancellation
US20120016921A1 (en) * 2009-03-19 2012-01-19 Mark A Davenport Method and Apparatus for Compressive Domain Filtering and Interference Cancellation
US8566053B2 (en) * 2009-03-19 2013-10-22 William Marsh Rice University Method and apparatus for compressive parameter estimation and tracking
US7834795B1 (en) 2009-05-28 2010-11-16 Bae Systems Information And Electronic Systems Integration Inc. Compressive sensor array system and method
US20100302086A1 (en) * 2009-05-28 2010-12-02 Bae Systems Information And Electronic Systems Integration, Inc. Compressive sensor array system and method
US7916052B2 (en) 2009-07-13 2011-03-29 L3 Communications Integrated Systems, L.P. Compressed sensing characterization system and method
US20110007795A1 (en) * 2009-07-13 2011-01-13 L3 Communications Intergrated Systems, L.P. Compressed sensing characterization system and method
US8761525B2 (en) 2009-11-20 2014-06-24 Tripurari Singh Method and system for compressive color image sampling and reconstruction
US20110142339A1 (en) * 2009-11-20 2011-06-16 Tripurari Singh Method and System for Compressive Color Image Sampling and Reconstruction
US9124755B2 (en) * 2009-12-07 2015-09-01 William Marsh Rice University Apparatus and method for compressive imaging and sensing through multiplexed modulation
US9521306B2 (en) 2009-12-07 2016-12-13 William Marsh Rice University Apparatus and method for compressive imaging and sensing through multiplexed modulation via spinning disks
WO2011071958A2 (fr) * 2009-12-07 2011-06-16 William Marsh Rice University Appareil et procédé d'imagerie compressive et de détection par modulation multiplexée
US20120314099A1 (en) * 2009-12-07 2012-12-13 Kevin F Kelly Apparatus And Method For Compressive Imaging And Sensing Through Multiplexed Modulation
WO2011071958A3 (fr) * 2009-12-07 2011-10-20 William Marsh Rice University Appareil et procédé d'imagerie compressive et de détection par modulation multiplexée
WO2011103601A2 (fr) * 2010-02-22 2011-08-25 William Marsh Rice University Nombre augmenté de pixels dans des matrices de détecteurs à l'aide d'une détection de compression
WO2011103601A3 (fr) * 2010-02-22 2011-11-17 William Marsh Rice University Nombre augmenté de pixels dans des matrices de détecteurs à l'aide d'une détection de compression
GB2491756A (en) * 2010-02-22 2012-12-12 Univ Rice William M Improved number of pixels in detector arrays using compressive sensing
WO2012044380A3 (fr) * 2010-06-18 2012-06-14 William Marsh Rice University Procédé et appareil pour une acquisition compressive et une récupération d'images dynamiques
WO2012044380A2 (fr) * 2010-06-18 2012-04-05 William Marsh Rice University Procédé et appareil pour une acquisition compressive et une récupération d'images dynamiques
US9654752B2 (en) * 2010-06-18 2017-05-16 William Marsh Rice University Method and apparatus for compressive acquisition and recovery of dynamic imagery
US20130093957A1 (en) * 2010-06-18 2013-04-18 Richard G. Baraniuk Method and apparatus for compressive acquisition and recovery of dynamic imagery
US20120038789A1 (en) * 2010-08-11 2012-02-16 Kelly Kevin F Determining Light Level Variation in Compressive Imaging by Injecting Calibration Patterns into Pattern Sequence
US8717463B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation Adaptively filtering compressive imaging measurements to attenuate noise
US8885073B2 (en) 2010-08-11 2014-11-11 Inview Technology Corporation Dedicated power meter to measure background light level in compressive imaging system
US8760542B2 (en) * 2010-08-11 2014-06-24 Inview Technology Corporation Compensation of compressive imaging measurements based on measurements from power meter
US20120038805A1 (en) * 2010-08-11 2012-02-16 Kelly Kevin F Dynamic Range Optimization and Hot Spot Correction in a Compressive Imaging System
US8634009B2 (en) * 2010-08-11 2014-01-21 Inview Technology Corporation Dynamic range optimization in a compressive imaging system
US8717484B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation TIR prism to separate incident light and modulated light in compressive imaging device
US8860835B2 (en) * 2010-08-11 2014-10-14 Inview Technology Corporation Decreasing image acquisition time for compressive imaging devices
US8717492B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation Focusing mechanisms for compressive imaging device
US8717466B2 (en) 2010-08-11 2014-05-06 Inview Technology Corporation Dual-port measurements of light reflected from micromirror array
US8922688B2 (en) 2010-08-11 2014-12-30 Inview Technology Corporation Hot spot correction in a compressive imaging system
US8570405B2 (en) * 2010-08-11 2013-10-29 Inview Technology Corporation Determining light level variation in compressive imaging by injecting calibration patterns into pattern sequence
US8570406B2 (en) 2010-08-11 2013-10-29 Inview Technology Corporation Low-pass filtering of compressive imaging measurements to infer light level variation
WO2012021450A1 (fr) 2010-08-11 2012-02-16 Inview Technology Corporation Diminution du temps d'acquisition d'images pour des dispositifs de formation d'images à compression
US20120038786A1 (en) * 2010-08-11 2012-02-16 Kelly Kevin F Decreasing Image Acquisition Time for Compressive Imaging Devices
US8472736B2 (en) 2010-09-30 2013-06-25 The Charles Stark Draper Laboratory, Inc. Attitude estimation by reducing noise with dragback
CN103181159A (zh) * 2010-09-30 2013-06-26 阿尔卡特朗讯公司 使用空间和时间积分产生视频的压缩测量的方法和装置
US8644376B2 (en) * 2010-09-30 2014-02-04 Alcatel Lucent Apparatus and method for generating compressive measurements of video using spatial and temporal integration
US20120082205A1 (en) * 2010-09-30 2012-04-05 Alcatel-Lucent Usa Inc. Apparatus and method for generating compressive measurements of video using spatial and temporal integration
US20120082393A1 (en) * 2010-09-30 2012-04-05 The Charles Stark Draper Laboratory, Inc. Attitude estimation with compressive sampling of starfield data
WO2012044468A1 (fr) * 2010-09-30 2012-04-05 Alcatel Lucent Appareil et procédé pour générer des mesures de compression d'une vidéo utilisant une intégration spatiale et temporelle
US9344736B2 (en) 2010-09-30 2016-05-17 Alcatel Lucent Systems and methods for compressive sense imaging
US8929456B2 (en) 2010-09-30 2015-01-06 Alcatel Lucent Video coding using compressive measurements
US9634690B2 (en) 2010-09-30 2017-04-25 Alcatel Lucent Method and apparatus for arbitrary resolution video coding using compressive sampling measurements
US8472735B2 (en) * 2010-09-30 2013-06-25 The Charles Stark Draper Laboratory, Inc. Attitude estimation with compressive sampling of starfield data
US8472737B2 (en) 2010-09-30 2013-06-25 The Charles Stark Draper Laboratory, Inc. Attitude estimation in compressed domain
US8884975B2 (en) * 2010-11-19 2014-11-11 Ricoh Company, Ltd. Image projection apparatus, memory control apparatus, laser projector, and memory access method
US20120127184A1 (en) * 2010-11-19 2012-05-24 Ricoh Company, Ltd. Image projection apparatus, memory control apparatus, laser projector, and memory access method
US8704912B2 (en) * 2011-01-20 2014-04-22 Canon Kabushiki Kaisha Apparatus and method for compressive sampling
US20120188433A1 (en) * 2011-01-20 2012-07-26 Canon Kabushiki Kaisha Apparatus and method for compressive sampling
US9875349B2 (en) 2011-03-16 2018-01-23 Apple Inc. Locking and unlocking a mobile device using facial recognition
US9477829B2 (en) * 2011-03-16 2016-10-25 Apple Inc. Locking and unlocking a mobile device using facial recognition
US10878069B2 (en) 2011-03-16 2020-12-29 Apple Inc. Locking and unlocking a mobile device using facial recognition
EP2685227A1 (fr) * 2011-04-25 2014-01-15 Center For Space Science And Applied Research, Chinese Academy of Sciences Système d'imagerie par comptage monophotonique et procédé associé
EP2685227A4 (fr) * 2011-04-25 2014-10-01 Ct Space Sci & Applied Res Cas Système d'imagerie par comptage monophotonique et procédé associé
US8723130B2 (en) 2011-04-25 2014-05-13 Center For Space Science And Applied Research, Chinese Academy Of Sciences Single photon-counting imaging system and method thereof
JP2014512540A (ja) * 2011-04-25 2014-05-22 センター フォー スペース サイエンス アンド アプライド リサーチ、 チャイニーズ アカデミー オブ サイエンスズ 単一光子計数イメージングシステム及び方法
WO2012146156A1 (fr) * 2011-04-25 2012-11-01 中国科学院空间科学与应用研究中心 Système d'imagerie par comptage monophotonique et procédé associé
WO2012174940A1 (fr) * 2011-06-20 2012-12-27 中国科学院空间科学与应用研究中心 Procédé d'imagerie multispectrale pour émission photonique ultrafaible et système associé
EP2728324A1 (fr) * 2011-06-20 2014-05-07 Center For Space Science And Applied Research, Chinese Academy of Sciences Procédé d'imagerie multispectrale pour émission photonique ultrafaible et système associé
EP2728324A4 (fr) * 2011-06-20 2015-02-18 Ct Space Sci & Applied Res Cas Procédé d'imagerie multispectrale pour émission photonique ultrafaible et système associé
US9807317B2 (en) * 2011-06-20 2017-10-31 Center For Space Science And Applied Research, Chinese Academy Of Sciences Multi-spectral imaging method for ultraweak photon emission and system thereof
US20140043486A1 (en) * 2011-06-20 2014-02-13 Guangjie Zhai Multi-Spectral Imaging Method for Ultraweak Photon Emission and System Thereof
US20130002968A1 (en) * 2011-06-28 2013-01-03 Bridge Robert F User Control of the Visual Performance of a Compressive Imaging System
US9325947B2 (en) * 2011-06-28 2016-04-26 Inview Technology Corporation High-speed event detection using a compressive-sensing hyperspectral-imaging architecture
US9160914B2 (en) * 2011-06-28 2015-10-13 Inview Technology Corporation User control of the visual performance of a compressive imaging system
US20130128042A1 (en) * 2011-06-28 2013-05-23 InView Techonogogy Corporation High-speed event detection using a compressive-sensing hyperspectral-imaging architecture
WO2013003485A1 (fr) * 2011-06-28 2013-01-03 Inview Technology Corporation Reconstruction de séquence d'images s'appuyant sur des sous-ensembles de mesures chevauchants
US20130002715A1 (en) * 2011-06-28 2013-01-03 Tidman James M Image Sequence Reconstruction based on Overlapping Measurement Subsets
US9398310B2 (en) 2011-07-14 2016-07-19 Alcatel Lucent Method and apparatus for super-resolution video coding using compressive sampling measurements
US9759995B2 (en) 2011-08-18 2017-09-12 Massachusetts Institute Of Technology System and method for diffuse imaging with time-varying illumination intensity
EP2755327A4 (fr) * 2011-10-25 2015-11-04 Ct Space Sci & Applied Res Cas Système et procédé à spectre d'imagerie multidimensionnelle à photon unique à résolution temporelle ou à lumière ultrafaible
US20140253713A1 (en) * 2011-10-25 2014-09-11 Guangjie Zhai Time-Resolved Single-Photon or Ultra-Weak Light Multi-Dimensional Imaging Spectrum System and Method
JP2014531032A (ja) * 2011-10-25 2014-11-20 センター フォー スペース サイエンス アンド アプライド リサーチ、 チャイニーズ アカデミー オブ サイエンスズＣｅｎｔｅｒ Ｆｏｒ Ｓｐａｃｅ Ｓｃｉｅｎｃｅ Ａｎｄ Ａｐｐｌｉｅｄ Ｒｅｓｅａｒｃｈ， Ｃｈｉｎｅｓｅ Ａｃａｄｅｍｙ Ｏｆ Ｓｃｉｅｎｃｅｓ 時間分解単一光子又は極微弱光多次元イメージングスペクトルシステム及び方法
US9448162B2 (en) * 2011-10-25 2016-09-20 Center For Space Science And Applied Research, Chinese Academy Of Sciences Time-resolved single-photon or ultra-weak light multi-dimensional imaging spectrum system and method
CN104054266A (zh) * 2011-10-25 2014-09-17 中国科学院空间科学与应用研究中心 一种时间分辨单光子或极弱光多维成像光谱系统及方法
WO2013066896A1 (fr) * 2011-10-31 2013-05-10 Inview Technology Corporation Détection rapide d'évènement à l'aide d'une architecture d'imagerie hyperstructurale à acquisition comprimée
US20150036021A1 (en) * 2011-11-10 2015-02-05 Centre National De La Recherche Scientifique - Cnrs Multiple Scattering Medium For Compressive Imaging
US20160006916A1 (en) * 2012-02-07 2016-01-07 Alcatel-Lucent Usa Inc. Lensless compressive image acquisition
US20140063314A1 (en) * 2012-02-28 2014-03-06 Aswin C Sankaranarayanan System And Method Of Video Compressive Sensing For Spatial-Multiplexing Cameras
US9674436B2 (en) * 2012-06-18 2017-06-06 Microsoft Technology Licensing, Llc Selective imaging zones of an imaging sensor
US20130335546A1 (en) * 2012-06-18 2013-12-19 Randall T. Crane Selective imaging
US9398229B2 (en) 2012-06-18 2016-07-19 Microsoft Technology Licensing, Llc Selective illumination of a region within a field of view
US10063846B2 (en) 2012-06-18 2018-08-28 Microsoft Technology Licensing, Llc Selective illumination of a region within a field of view
US20140036041A1 (en) * 2012-08-03 2014-02-06 Leung CHI WAI Digital camera, laminated photo printer and system for making 3d color pictures
US8913835B2 (en) * 2012-08-03 2014-12-16 Kodak Alaris Inc. Identifying key frames using group sparsity analysis
US20140037215A1 (en) * 2012-08-03 2014-02-06 Mrityunjay Kumar Identifying key frames using group sparsity analysis
US9451241B2 (en) * 2012-08-03 2016-09-20 Leung Chi Wai Digital camera, laminated photo printer and system for making 3D color pictures
US9071739B2 (en) 2012-08-30 2015-06-30 Google Inc. Single pixel camera
US8958005B1 (en) * 2012-08-30 2015-02-17 Google Inc. Single pixel camera
US8907280B1 (en) 2012-09-19 2014-12-09 Sandia Corporation Fast electron microscopy via compressive sensing
US9319578B2 (en) 2012-10-24 2016-04-19 Alcatel Lucent Resolution and focus enhancement
US9092890B2 (en) 2012-12-20 2015-07-28 Ricoh Company, Ltd. Occlusion-aware reconstruction of three-dimensional scenes from light field images
US9277139B2 (en) * 2013-01-16 2016-03-01 Inview Technology Corporation Generating modulation patterns for the acquisition of multiscale information in received signals
US20140198236A1 (en) * 2013-01-16 2014-07-17 Inview Technology Corporation Generating Modulation Patterns for the Acquisition of Multiscale Information in Received Signals
CN103115681A (zh) * 2013-01-24 2013-05-22 中国科学院空间科学与应用研究中心 一种超灵敏时间分辨光谱仪及其时间分辨方法
CN103115680B (zh) * 2013-01-24 2014-11-12 中国科学院空间科学与应用研究中心 一种超灵敏光谱仪以及光谱检测方法
CN103115680A (zh) * 2013-01-24 2013-05-22 中国科学院空间科学与应用研究中心 一种超灵敏光谱仪以及光谱检测方法
US10616464B2 (en) 2013-02-15 2020-04-07 Apple Inc. Apparatus and method for automatically activating a camera application based on detecting an intent to capture a photograph or a video
US9875428B2 (en) 2013-03-15 2018-01-23 Arizona Board Of Regents On Behalf Of Arizona State University Ensemble sparse models for image analysis and restoration
WO2014144306A1 (fr) * 2013-03-15 2014-09-18 Arizona Board Of Regents On Behalf Of Arizona State University Modèles clairsemés d'ensemble pour analyse et restauration d'image
GB2526737B (en) * 2013-03-27 2020-05-13 Toshiba Res Europe Limited Signal processing method and apparatus
WO2014155026A1 (fr) * 2013-03-27 2014-10-02 Toshiba Research Europe Limited Procédé et appareil de traitement de signal
GB2526737A (en) * 2013-03-27 2015-12-02 Toshiba Res Europ Ltd Signal processing method and apparatus
US9503287B2 (en) 2013-03-27 2016-11-22 Kabushiki Kaisha Toshiba Signal processing method and apparatus
US9412185B2 (en) 2013-07-01 2016-08-09 Xerox Corporation Reconstructing an image of a scene captured using a compressed sensing device
US20150003750A1 (en) * 2013-07-01 2015-01-01 Xerox Corporation Reconstructing an image of a scene captured using a compressed sensing device
US9070218B2 (en) * 2013-07-01 2015-06-30 Xerox Corporation Reconstructing an image of a scene captured using a compressed sensing device
US10062157B2 (en) * 2013-10-18 2018-08-28 Kla-Tencor Corporation Compressive sensing for metrology
US9518916B1 (en) * 2013-10-18 2016-12-13 Kla-Tencor Corporation Compressive sensing for metrology
US20170076440A1 (en) * 2013-10-18 2017-03-16 Kla-Tencor Corporation Compressive sensing for metrology
CN103630240A (zh) * 2013-11-14 2014-03-12 中国科学院上海光学精密机械研究所 物体表面颜色测量装置与方法
US9563806B2 (en) 2013-12-20 2017-02-07 Alcatel Lucent Methods and apparatuses for detecting anomalies using transform based compressed sensing matrices
US9600899B2 (en) 2013-12-20 2017-03-21 Alcatel Lucent Methods and apparatuses for detecting anomalies in the compressed sensing domain
US20150215529A1 (en) * 2014-01-24 2015-07-30 Goodrich Corporation Random imaging
US9300865B2 (en) * 2014-01-24 2016-03-29 Goodrich Corporation Random imaging
US9679093B2 (en) * 2014-02-20 2017-06-13 West Virginia University Selective wakeup of digital sensing and processing systems using reconfigurable analog circuits
US20170255252A1 (en) * 2014-02-20 2017-09-07 West Virginia University Selective wakeup of digital sensing and processing systems using reconfigurable analog circuits
US10088893B2 (en) * 2014-02-20 2018-10-02 West Virginia University Selective wakeup of digital sensing and processing systems using reconfigurable analog circuits
US20150234443A1 (en) * 2014-02-20 2015-08-20 West Virginia University Selective wakeup of digital sensing and processing systems using reconfigurable analog circuits
US9719940B2 (en) * 2014-03-20 2017-08-01 KLA—Tencor Corporation Compressive sensing with illumination patterning
US9607362B2 (en) * 2014-05-16 2017-03-28 North Carolina State University Compressive imaging using approximate message passing with denoising
US20160048950A1 (en) * 2014-05-16 2016-02-18 North Carolina State University Compressive imaging using approximate message passing with denoising
CN103968945A (zh) * 2014-05-28 2014-08-06 中国科学院空间科学与应用研究中心 基于二阶压缩感知的超灵敏光谱成像天文望远镜及方法
CN104019899A (zh) * 2014-05-28 2014-09-03 中国科学院空间科学与应用研究中心 一种超灵敏天文望远镜及其天文图像获取方法
EP3161520A4 (fr) * 2014-06-27 2018-03-14 HRL Laboratories, LLC Lidar à balayage compressif
US9894324B2 (en) 2014-07-15 2018-02-13 Alcatel-Lucent Usa Inc. Method and system for modifying compressive sensing block sizes for video monitoring using distance information
WO2016028200A1 (fr) * 2014-08-21 2016-02-25 Totalförsvarets Forskningsinstitut Système d'imagerie de parallélisation d'imagerie par acquisition comprimée
US20160065824A1 (en) * 2014-08-26 2016-03-03 Sensors Unlimited, Inc. High dynamic range image sensor
US9467628B2 (en) * 2014-08-26 2016-10-11 Sensors Unlimited, Inc. High dynamic range image sensor
CN104320567A (zh) * 2014-10-29 2015-01-28 中国科学院半导体研究所 数字微镜阵列编码闪光三维成像方法及装置
US9615022B2 (en) 2014-12-11 2017-04-04 Conduent Business Services, Llc High-resolution imaging devices using low-resolution sensors and compressive sensing exploiting joint sparsity
CN104702828A (zh) * 2015-03-13 2015-06-10 四川大学 一种分块阵列块内压缩感知逐点扫描相机
US20160276050A1 (en) * 2015-03-18 2016-09-22 Battelle Memorial Institute Electron beam masks for compressive sensors
US10224175B2 (en) 2015-03-18 2019-03-05 Battelle Memorial Institute Compressive transmission microscopy
US10170274B2 (en) * 2015-03-18 2019-01-01 Battelle Memorial Institute TEM phase contrast imaging with image plane phase grating
US10109453B2 (en) * 2015-03-18 2018-10-23 Battelle Memorial Institute Electron beam masks for compressive sensors
US20170025247A1 (en) * 2015-03-18 2017-01-26 Battelle Memorial Institute Tem phase contrast imaging with image plane phase grating
US10140249B2 (en) 2015-06-05 2018-11-27 North Carolina State University Approximate message passing with universal denoising
US10502990B2 (en) 2015-06-22 2019-12-10 Samsung Display Co., Ltd. Liquid crystal display comprising first, second, and third pixels having driving voltages for maximum transmittances that are different from each other
US11050963B2 (en) 2015-07-01 2021-06-29 Massachusetts Institute Of Technology Method and apparatus for on-chip per-pixel pseudo-random time coded exposure
US10616520B2 (en) * 2015-07-01 2020-04-07 Massachusetts Institute Of Technology Method and apparatus for on-chip per-pixel pseudo-random time coded exposure
US9667456B2 (en) * 2015-08-05 2017-05-30 National Taiwan University Compressive sensing system based on personalized basis and method thereof
US20170041166A1 (en) * 2015-08-05 2017-02-09 National Taiwan University Compressive sensing system based on personalized basis and method thereof
CN105205496A (zh) * 2015-09-11 2015-12-30 重庆邮电大学 基于空间信息约束的增强型稀疏表示高光谱图像分类装置及方法
CN105205496B (zh) * 2015-09-11 2018-12-28 重庆邮电大学 增强型稀疏表示高光谱图像分类装置及方法
US10136116B2 (en) 2016-03-07 2018-11-20 Ricoh Company, Ltd. Object segmentation from light field data
US9990699B1 (en) * 2016-03-21 2018-06-05 Rockwell Collins, Inc. Block match denoising for real-time imaging
US10580614B2 (en) 2016-04-29 2020-03-03 Battelle Memorial Institute Compressive scanning spectroscopy
US20170357000A1 (en) * 2016-06-09 2017-12-14 Texas Instruments Incorporated Processing techniques for lidar receiver using spatial light modulators
CN107796788A (zh) * 2016-08-29 2018-03-13 南京理工大学 基于变分贝叶斯期望最大算法的传感矩阵测量方法
WO2018134729A1 (fr) * 2017-01-18 2018-07-26 Technion Research & Development Foundation Ltd. Imagerie à super-résolution ultrasonore basée sur la rareté
US10295677B2 (en) 2017-05-08 2019-05-21 Battelle Memorial Institute Systems and methods for data storage and retrieval
US10656287B2 (en) 2017-05-08 2020-05-19 Battelle Memorial Institute Systems and methods for data storage and retrieval
US10088753B1 (en) * 2017-05-17 2018-10-02 Powerchip Technology Corporation Reconfiguring image brightness module and related method
CN111226141A (zh) * 2017-10-20 2020-06-02 澳大利亚核科学和技术组织 压缩成像方法和系统
US10818009B2 (en) * 2017-11-10 2020-10-27 Shanghai United Imaging Healthcare Co., Ltd. System and method for image reconstruction
US11398030B2 (en) 2017-11-10 2022-07-26 Shanghai United Imaging Healthcare Co., Ltd. System and method for image reconstruction
US20190147589A1 (en) * 2017-11-10 2019-05-16 Shenzhen United Imaging Healthcare Co., Ltd. System and method for image reconstruction
WO2020020805A1 (fr) 2018-07-23 2020-01-30 Politecnico Di Milano Dispositif et procédé de détection de données optiques à résolution temporelle
US11788960B2 (en) 2018-07-23 2023-10-17 Politecnico Di Milano Device and method for detecting time-resolved optical data
CN110166678A (zh) * 2019-06-26 2019-08-23 京东方科技集团股份有限公司 图像采集结构及其采集方法、显示装置
CN110632000A (zh) * 2019-09-06 2019-12-31 中国科学院西安光学精密机械研究所 基于压缩感知的动态双臂多通道凝视光谱成像系统
US20220417407A1 (en) * 2019-12-25 2022-12-29 Hamamatsu Photonics K.K. Imaging device and imaging method
US11852795B2 (en) 2019-12-25 2023-12-26 Hamamatsu Photonics K.K. Imaging device and imaging method
US11962913B2 (en) * 2019-12-25 2024-04-16 Hamamatsu Photonics K.K. Imaging device comprising spatial light modulator and imaging method thereof
CN113050872A (zh) * 2019-12-26 2021-06-29 财团法人工业技术研究院 传感器上的数据处理系统及其方法与去识别化传感装置
CN111640069A (zh) * 2020-04-17 2020-09-08 上海交通大学 基于光感知网络和相位补偿的压缩成像方法、系统和装置
CN113890801A (zh) * 2021-12-08 2022-01-04 浙大城市学院 采用频域编码混频的压缩感知信号获取系统及方法

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US8199244B2 (en) 2012-06-12
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US8848091B2 (en) 2014-09-30
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US20110025870A1 (en) 2011-02-03

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