Method and system for optical imaging and ranging

• US 7,705,970 B2
• Filed: 06/05/2007
• Issued: 04/27/2010
• Est. Priority Date: 06/05/2006
• Status: Active Grant

• Current Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Greengard, Adam D., Cogswell, Carol J., Schechner, Yoav Y., Piestun, Rafael
• Primary Examiner(s): Tarcza; Thomas H
• Assistant Examiner(s): Ratcliffe; Luke D
• Application Number:
  US11/758,472
• Publication Number:
  US 20080137059A1
• Time in Patent Office: 1,057 Days
• Field of Search: 356 301- 315, 356 401- 41, 356 501- 515, 356 6- 22, 356/128, 356/128.5, 356/139.09, 356/139.1
• US Class Current: 356/4.01
• CPC Class Codes: G01C 3/08 Use of electric radiation detectors

Abstract

The distance of objects to an optical system is estimated. An optical mask such as a diffractive optical element, continuous phase mask, hologram, amplitude mask, or combination thereof is placed within the optics in front of a sensor array such as a CCD, CID or COMAS device. The optical mask encodes the three-dimensional response of the system. The mask is designed to optimize depth estimation, for example, by maximizing Fisher information. A particular implementation creates a point spread function (“PSF”) that rotates as a function of the object position. The image or images obtained with different PSFs may be digitally processed to recover both a depth map of the scene and other parameters such as image brightness. The digital processing used to recover the depth map of the object may include deconvolution of a PSF from detected images.

Claims

1. A system for estimating a distance to an object, the system comprising:

an imaging system;

a sensor array, wherein the sensor array is disposed to receive one or more images of the object through the imaging system;

a one or more masks disposed within the imaging system in front of the sensor array, the one or more masks being configured to encode one or more three-dimensional responses of the imaging system for calculating a three dimensional point spread function that evolves as a function of a position of the object.

2. The system recited in claim 1, wherein the mask maximizes Fisher information.

3. The system recited in claim 1, wherein the mask optimizes Fisher information.

4. The system recited in claim 1, further comprising:

recovering a plurality of images of the object with the sensor array; and

calculating a deconvolution of the plurality of images.

5. The system recited in claim 4, wherein recovering the plurality of images of the object comprises recovering the plurality of images sequentially.

6. The system recited in claim 4, wherein recovering the plurality of images of the object comprises recovering the plurality of images substantially simultaneously.

7. The system recited in claim 4, wherein at least two of the plurality of images are recovered from different masks.

8. The system recited in claim 4, further comprising regularizing the deconvolution.

9. A method for estimating a distance to an object, the method comprising:

detecting light from the object through an imaging system at a sensor array, the imaging system including a first mask disposed in front of the sensor array and encoded with a first point spread function and a second mask disposed in front of the sensor array and encoded with a second point spread function, wherein either or both of the first point spread function and the second point spread function comprises a three dimensional point spread function that evolves as a function of a position of the object;

receiving a first image of the object through the first mask;

receiving a second image of the object through the second mask;

calculating a deconvolution of the first image and the second image; and

estimating the distance of the object using the deconvolved of the first image and the second image.

10. The method according to claim 9, further comprising estimating the angle of rotation of the deconvolution of the first image and the second image.

11. The method according to claim 9, wherein the distance of the object is estimated using an estimation scheme selected from the group consisting of a maximum likelihood estimation, a maximum a-posteriori estimation, and a Bayesian estimation.

12. The method according to claim 9, wherein the calculating a deconvolution of the first image and the second image comprises estimating the point spread function from the first image, the second image, a first optical transfer function associated with the first point spread function, and a second optical transfer function associated with the second point spread function.

13. The method according to claim 9, wherein the estimating the point spread function comprises calculating the point spread function from the following equation:

${\hat{h}}_{\mathrm{rot}}<br><br>\left(\mathrm{\psi <br><br>}\right)=F^{-1}<br><br>\left\{\frac{I_{\mathrm{first}}^{*}}{{<br><br>I_{\mathrm{second}}<br><br>}^2+{\mathrm{\sigma <br><br>}}^2}<br><br>I_{\mathrm{first}}<br><br>H_{\mathrm{second}}\right\}.$

14. The method according to claim 10, wherein the estimating the angle of rotation of the deconvolution of the first image and the second image comprises:

estimating the location of the two largest local maxima of the deconvolution first image and the second image;

estimating the center of the two largest local maxima; and

estimating the angle of rotation between the center of the two largest local maxima.

15. The method according to claim 10, wherein the estimating the distance to the object from the angle of rotation of the deconvolved rotating point spread function comprises:

comparing the angle of rotation of the deconvolution of the first image and the second image with a calibration curve that relates rotation to depth; and

estimating the depth associated with the angle of rotation of the deconvolution of the first image and the second image.

16. The method according to claim 15, wherein the estimating the depth associated with angle of rotation of the deconvolved rotating point spread function is calculated using the following function:

${\hat{z}}_{\mathrm{obj}}^{\mathrm{\prime <br><br>}}={\left(\frac{z_0<br><br>\tan <br><br>\frac{\mathrm{\varphi <br><br>}}{2}}{z_{\mathrm{img}}^2}+\frac{1}{z_{\mathrm{obj}}^{\mathrm{focus}}}\right)}^{-1}.$

17. The method recited in claim 9, further comprising estimating a brightness of the object from one of the first point spread function or the second point spread function.

18. The method recited in claim 12, further comprising estimating a brightness of the object from one of the first point spread function or the second point spread function.

19. An imaging system for estimating a distance to an object, the system comprising:

a sensor array;

a mask disposed within the imaging system in front of the sensor array, the mask encoded with a three-dimensional response of the imaging system corresponding with a rotating point spread function that evolves as a function of the distance to the object; and

computational logic configured to receive images recorded by the imaging system and compute the distance to the object, wherein the distance to the object is calculated by deconvolution of the rotating point spread function of the imaging system from at least the rotating PSF image, and estimating the distance to the object from the rotating point spread function of the imaging system.

20. The system recited in claim 19, wherein the computational logic is configured to:

estimate the angle of rotation of the deconvolved rotating point spread function; and

estimate the distance to the object from the ange of rotation of the deconvolved rotating point spread function.

21. The system recited in claim 19, wherein the computational logic is configured to estimate the distance of the object using an estimation scheme selected from the group consisting of a maximum likelihood estimation, a maximum a-posteriori estimation, and a Bayesian estimation.

22. The system recited in claim 19, wherein the mask maximizes Fisher information.

23. The system recited in claim 19, wherein the mask is adapted to be statically or dynamically changed.

24. The system recited in claim 19, wherein the mask is a computer generated hologram.

25. The system recited in claim 19, wherein the mask is selected from the group consisting of a diffractive optical element, a continuous phase mask, a hologram, an amplitude mask, a kinoform, and a combination thereof.

26. The system recited in claim 19, wherein the imaging system is an optical system.

27. The system recited in claim 19, wherein the sensor array is selected from the group consisting of a CCD, CID, and CMOS device.

28. A method for estimating a distance to an object with an imaging system employing a rotating point spread function, the method comprising:

detecting a rotating PSF image of the object from the imaging system;

deconvolving the rotating point spread function of the imaging system from at least the rotating PSF image, wherein the deconvolution returns a deconvolved rotating point spread function;

estimating the angle of rotation of the deconvolved rotating point spread function;

estimating the distance to the object from the angle of rotation of the deconvolved rotating point spread function; and

returning the distance to the object.