METHOD AND APPARATUS FOR COMPRESSIVE IMAGING DEVICE

• US 20110025870A1
• Filed: 06/01/2010
• Published: 02/03/2011
• Est. Priority Date: 04/21/2005
• Status: Active Grant

• Current Assignee: Rice University
• Original Assignee: Rice University
• Inventors: Laska, Jason N., Lane, Courtney C., Baraniuk, Richard G., Kelly, Kevin F., Takhar, Dharmpal, Baron, Dror Z., Wakin, Michael B., Duarte, Marco F.
• Granted Patent: US 8,199,244 B2
• Time in Patent Office: Days
• US Class Current: 348/222.1
• CPC Class Codes: H04L 25/20 Repeater circuits; Relay circuits
• CPC Class Codes: H04N 25/00 Circuitry of solid-state image sensors [SSIS]; Control thereof
• CPC Class Codes: H04N 3/08 having a moving reflector

Abstract

A new digital image/video camera that directly acquires random projections of the incident light field without first collecting the pixels/voxels. In one preferred embodiment, the camera employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtain an image with only a single detection element while measuring the image/video fewer times than the number of pixels or voxels—this can significantly reduce the computation required for image/video acquisition/encoding. Since the system features a single photon detector, it can also be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.

Claims

1. A method for acquiring an image or video sequence from inner products comprising the steps of:

modulating an incident light field by a series of patterns with a modulator;

optically computing inner products between the light field of said image and said series of patterns with an encoder; and

reconstructing an image or video sequence based upon said inner products from said encoder;

wherein said reconstructing step is based on at least one of a Greedy reconstruction algorithm, Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit, group testing, LASSO, LARS, expectation-maximization, Bayesian estimation algorithm, belief propagation, wavelet-structure exploiting algorithm, Sudocode reconstruction, reconstruction based on manifolds, l₁ reconstruction, l₀ reconstruction, and l₂ reconstruction.

2. A method for acquiring an image or video sequence from inner products according to claim 1 wherein said image or video sequence is reconstructed with fewer inner products than a number of pixels or voxels reconstructed.

3. A method for acquiring an image or video sequence according to claim 1, wherein said step of optically computing inner products comprises integrating the modulated light field at a plurality of points.

4. A method for acquiring an image or video sequence according to claim 1, wherein said series of patterns comprises random patterns.

19. A method for acquiring an image or video sequence from inner products according to claim 1 wherein said series of pattern is incoherent with a sparsity basis suitable for said image or video sequence.

5. An imaging system comprising:

means for modulating an incident light field by a series of patterns;

means for optically computing inner products between the light field and said series of patterns; and

means for recovering a signal based upon said inner products and at least one of a Greedy reconstruction algorithm, Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit, group testing, LASSO, LARS, expectation-maximization, Bayesian estimation algorithm, belief propagation, wavelet-structure exploiting algorithm, Sudocode reconstruction, reconstruction based on manifolds, l₁ reconstruction, l₀ reconstruction, and l₂ reconstruction.

6. An imaging system according to claim 5, wherein said means for modulating comprises at least one of a digital micromirror device array, an LCD shutter array, and a physically moving shutter array.

7. An imaging system according to claim 5, wherein said means for recovering a signal comprises:

a single sensing element, wherein said single sensing element measures multiple pixels of an image;

a memory for recording intensity values from said single sensing element wherein a total number of intensity values recorded for said image is smaller than a total number of pixels in said image and said recorded intensity values do not correspond directly to values of pixel intensities of said image; and

a processor for applying a reconstruction algorithm to signals received from said single sensing element.

8. An imaging system according to claim 5, wherein said means for recovering a signal recovers a signal using fewer measurements than a number of pixels or voxels to be recovered.

9. An imaging system according to claim 5, wherein said means for recovering a signal comprises:

a plurality of sensing elements; and

a processor for applying a reconstruction algorithm to signals received from said plurality of sensing elements.

10. An imaging device comprising:

a lens;

a digital micromirror device array;

means for modulating mirror orientations of said digital micromirror device array in an incoherent pattern sequence;

a pattern generator for supplying incoherent patterns to said means for modulating;

a sensor element, wherein each different mirror pattern produces a voltage at the sensor element that corresponds to a measurement;

an analog-to-digital converter for quantizing said voltage; and

a reconstruction means for reconstructing an image based upon a bitstream received from said analog-to-digital converter;

wherein said reconstruction means comprises at least one of a Greedy reconstruction algorithm, Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit, group testing, LASSO, LARS, expectation-maximization, Bayesian estimation algorithm, belief propagation, wavelet-structure exploiting algorithm, Sudocode reconstruction, reconstruction based on manifolds, l₁ reconstruction, l₀ reconstruction, and l₂ reconstruction.

11. An imaging device according to claim 10, wherein said image is a color, multispectral or hyperspectral image or a color, multispectral or hyperspectral video sequence.

12. An imaging device according to claim 10, wherein said reconstruction means comprises a processor for performing a reconstruction algorithm.

13. An imaging device according to claim 10, where said lens comprises a biconvex lens.

14. An imaging device according to claim 10, further comprising a second lens for focusing an image onto said sensing element.

15. An imaging device comprising:

a lens;

a digital micromirror device array;

means for modulating mirror orientations of said digital micromirror device array in an incoherent pattern sequence;

a pattern generator for supplying incoherent patterns to said means for modulating;

a sensor element, wherein each different mirror pattern produces a voltage at the sensor element that corresponds to a measurement;

an analog-to-digital converter for quantizing said voltage; and

a reconstruction means for reconstructing an image based upon a bitstream received from said analog-to-digital converter;

wherein said means for modulating comprises measurement matrices having most adjacent rows similar to one another.

16. An imaging device comprising:

a lens;

a digital micromirror device array;

means for modulating mirror orientations of said digital micromirror device array in an incoherent pattern sequence;

a pattern generator for supplying incoherent patterns to said means for modulating;

a sensor element, wherein each different mirror pattern produces a voltage at the sensor element that corresponds to a measurement;

an analog-to-digital converter for quantizing said voltage; and

a reconstruction means for reconstructing an image based upon a bitstream received from said analog-to-digital converter;

wherein said means for modulating comprises at least one of discrete valued measurement matrices, real valued measurement matrices, randomly distributed matrices with real values, randomly distributed matrices with discrete values, random Bernoulli-distributed matrices, random Gaussian-distributed matrices, permutation matrices, modulation matrices, structured binary matrices, sparse matrices, Sudocode matrices, code matrices, parity check matrices, and matrices having adjacent rows designed to minimize the power required to transition from one pattern to the next.

17. A method of acquiring a signal comprising the steps of:

measuring and encoding with an encoder projections of a signal that is compressible in a first set of basis functions onto a second set of basis functions, wherein said second set of basis functions is incoherent with said first set of basis functions;

reconstructing said signal based upon said projections;

wherein said reconstructing step is based on at least one of a Greedy reconstruction algorithm, Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit, group testing, LASSO, LARS, expectation-maximization, Bayesian estimation algorithm, belief propagation, wavelet-structure exploiting algorithm, Sudocode reconstruction, reconstruction based on manifolds, l₁ reconstruction, l₀ reconstruction, and l₂ reconstruction.

18. A method for acquiring a signal according to claim 17 wherein said signal is a signal of two or more dimensions.