Systems and methods for compressive sense imaging

• US 9,344,736 B2
• Filed: 06/26/2014
• Issued: 05/17/2016
• Est. Priority Date: 09/30/2010
• Status: Active Grant

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Jiang, Hong, Huang, Gang, Wilford, Paul Albin
• Primary Examiner(s): Tran, Phuoc
• Application Number:
  US14/315,909
• Publication Number:
  US 20150382000A1
• Time in Patent Office: 691 Days
• US Class Current: 1/1
• CPC Class Codes: H04N 19/169 characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
• CPC Class Codes: H04N 19/44 Decoders specially adapted therefor, e.g. video decoders which are asymmetric with respect to the encoder
• CPC Class Codes: H04N 19/48 using compressed domain processing techniques other than decoding, e.g. modification of transform coefficients, variable length coding [VLC] data or run-length data motion estimation in a transform domain H04N19/547; processing of decoded motion vectors H04N19/513
• CPC Class Codes: H04N 19/51 Motion estimation or motion compensation
• CPC Class Codes: H04N 19/85 using pre-processing or post-processing specially adapted for video compression

Abstract

Systems and methods for compressive sense imaging are provided. In one aspect, a system and method is provided for determining at least one kernel matrix based on the relative motion of an object during a time period corresponding to the acquisition of one or more compressive measurements of the object. An uncompressed image of the object is generated using the compressive measurements using a reconstruction basis matrix that is different from the compressive basis matrix used to acquire the compressive measurements of the object. To compensate for the relative motion of the object, the reconstruction basis matrix is determined based on the at least one kernel matrix and the compressive basis matrix.

Claims

1. A compressive sense imaging system, the system comprising:

a processing device configured to;

determine at least one kernel matrix based on relative motion of an object during a time period corresponding to acquisition of at least one of a plurality of compressive measurements representing a compressed image of the object;

determine a reconstruction basis matrix to compensate for the relative motion of the object during the time period based on the at least one kernel matrix; and

,

generate an uncompressed image of the object from the plurality of compressive measurements using the reconstruction basis matrix.

2. The compressive sense imaging system of claim 1, wherein the processing device is further configured to:

determine the reconstruction basis matrix by applying the at least one kernel matrix to at least one basis of a compressive basis matrix used to acquire the compressive measurements.

3. The compressive sense imaging system of claim 1, wherein the processing device is configured to:

determine the at least one kernel matrix such that a matrix operation between the at least one kernel matrix and a one-dimensional representation of the uncompressed image represents shifting the position of the object in the one-dimensional representation of the uncompressed image to a previous position of the object based on the relative motion of the object.

4. The compressive sense imaging system of claim 1, wherein the processing device is further configured to:

determine the relative motion of the object during the time period based on motion data from one or more sensors.

5. The compressive sense imaging system of claim 4, wherein the processing device is further configured to:

determine a degree of the relative motion of the object during the time period based on the motion data.

6. The compressive sense imaging system of claim 4, wherein the processing device is further configured to:

determine the at least one kernel matrix using the determined degree of relative motion of the object.

7. The compressive sense imaging system of claim 1, wherein the processing device is further configured to:

apply a sparsifying operator to generate the image of the object from the plurality of compressive measurements using the reconstruction basis matrix.

8. The compressive sense imaging system of claim 1, further comprising:

a camera unit configured to acquire the plurality of compressive measurements.

9. The compressive sense imaging system of claim 8, wherein the camera unit is a lensless camera unit.

10. The compressive sense imaging system of claim 8, wherein the camera unit comprises an array of selectable aperture elements and a detector, and wherein the processing device is further configured to:

selective enable or disable one or more of the aperture elements of the array based on at least one basis in a compressive basis matrix to acquire at least one of the plurality of compressive measurements during the time period, the at least one of the plurality of compressive measurements being determined based on an aggregated sum of light detected by the detector during the time period.

11. A method for compressive sense imaging, the method comprising:

determining, using a processor, at least one kernel matrix based on relative motion of an object during a time period corresponding to acquisition of at least one of a plurality of compressive measurements representing a compressed image of the object;

determining a reconstruction basis matrix to compensate for the relative motion of the object during the time period based on the at least one kernel matrix; and

,

generating an uncompressed image of the object from the plurality of compressive measurements using the reconstruction basis matrix.

12. The method of claim 11, wherein:

determining the reconstruction basis matrix further includes applying the at least one kernel matrix to at least one basis of a compressive basis matrix used to acquire the compressive measurements.

13. The method of claim 11, wherein:

determining the at least one kernel matrix includes determining the at least one kernel matrix such that a matrix operation between the at least one kernel matrix and a one-dimensional representation of the uncompressed image represents shifting the position of the object in the one-dimensional representation of the uncompressed image to a previous position of the object based on the relative motion of the object.

14. The method of claim 11, further comprising:

using motion data from one or more sensors for determining the relative motion of the object during the time period.

15. The method of claim 14, further comprising:

determining a degree of the relative motion of the object during the time period using the motion data.

16. The method of claim 15:

using the determined degree of relative motion of the object for determining the at least one kernel matrix.

17. The method of claim 11, further comprising:

applying a sparsifying operator for generating the image of the object from the plurality of compressive measurements using the reconstruction basis matrix.

18. The method of claim 11, further comprising:

acquiring the plurality of compressive measurements using a compressive basis matrix during the time period.

19. The method of claim 18, further comprising:

determining an aggregated sum of light output by a detector for acquiring at least one of the plurality of compressive measurements by enabling or disabling one or more aperture elements of an aperture array based on at least one basis in the compressive basis matrix during the time period.

20. A non-transitory computer-readable medium including one or more instructions for configuring a processor for:

determining at least one kernel matrix based on relative motion of an object during a time period corresponding to acquisition of at least one of a plurality of compressive measurements representing a compressed image of the object;

determining a reconstruction basis matrix to compensate for the relative motion of the object during the time period based on the at least one kernel matrix; and

,

generating an uncompressed image of the object from the plurality of compressive measurements using the reconstruction basis matrix.