Alignment of sharp and blurred images based on blur kernel sparseness

• US 8,238,694 B2
• Filed: 10/03/2008
• Issued: 08/07/2012
• Est. Priority Date: 10/03/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Sun, Jian, Shum, Heung-Yeung
• Primary Examiner(s): Le, Vu
• Assistant Examiner(s): Cese, Kenny
• Application Number:
  US12/245,339
• Publication Number:
  US 20100086232A1
• Time in Patent Office: 1,404 Days
• Field of Search: 382254-255, 382/237
• US Class Current: 382/294
• CPC Class Codes: G06T 7/35 using statistical methods

Abstract

The alignment of a sharp image of a subject and a blurred image of the same subject is disclosed. For example, one disclosed embodiment provides a method of determining a series of trial images. The method comprises applying a corresponding series of coordinate transforms to the sharp image, the series of coordinate transforms differing with respect to one or more of a rotational operation and a scaling operation. The method further comprises computing a series blur kernels corresponding to the series of trial images, each blur kernel mapping a trial image from the series of trial images to the blurred image. The method further includes locating a sparsest blur kernel in the series of blur kernels, and identifying one or more of the rotational operation and the scaling operation of the coordinate transform mapping the trial image corresponding to the sparsest blur kernel to the blurred image.

Claims

1. A method to align a first image and a second image of a subject, the method comprising:

for each of a series of blur kernels that vary according to alignment parameters, evaluating blur-kernel sparseness to locate a sparsest blur kernel, the sparsest blur kernel relating a select sharp image to a select blurred image, each of the select sharp image and select blurred image chosen from or computationally derived from the first or second images; and

applying an aligning operation to the first or second image, the aligning operation being dependent on the alignment parameters corresponding to the sparsest blur kernel.

2. The method of claim 1, wherein the aligning operation includes a rotational operation.

3. The method of claim 1, wherein the aligning operation includes a scaling operation.

4. The method of claim 1, wherein one or more of the first image and the second image is a fine image, and one or more of the select sharp image and the select blurred image is computationally derived from the fine image by downsampling.

5. The method of claim 1, further comprising:

applying a series of coordinate transforms based on the alignment parameters to the first image to yield a corresponding series of trial images;

computing a corresponding series of blur kernels relating the series of trial images to the second image; and

identifying within the series of coordinate transforms a select coordinate transform corresponding to the sparsest blur kernel.

6. The method of claim 5, wherein the aligning operation is at least partly based on the select coordinate transform.

7. The method of claim 5, wherein the second image is the select blurred image, wherein applying the select coordinate transform to the first image results in the select sharp image, and wherein and the series of trial images includes the select sharp image.

8. The method of claim 5, wherein the second image is the select sharp image, wherein applying the select coordinate transform to the first image results in the select blurred image, and wherein the series of trial images includes the select blurred image.

9. The method of claim 5, further comprising applying a translational alignment procedure to one or more of the first image and the second image.

10. A method to align a sharp image of a subject and a blurred image of the subject, the method comprising:

computing a series of trial images by applying a corresponding series of coordinate transforms to the sharp image, the series of coordinate transforms differing with respect to one or more of a rotational operation and a scaling operation;

computing a series blur kernels corresponding to the series of trial images, each blur kernel mapping a trial image from the series of trial images to the blurred image;

for each blur kernel in the series of blur kernels, evaluating blur-kernel sparseness to locate a sparsest blur kernel; and

identifying one or more of the rotational operation and the scaling operation of the coordinate transform mapping the trial image corresponding to the sparsest blur kernel to the blurred image.

11. The method of claim 10, wherein evaluating blur-kernel sparseness comprises evaluating a sparseness E(k) related to a blur-kernel size Z and to a blur-kernel distribution function p(k_i) according to

12. The method of claim 10, wherein evaluating blur-kernel sparseness comprises evaluating an entropy S(k) of a blur-kernel distribution function p(x) according to S(k)=−

∫

_xp(x)ln p(x).

13. The method of claim 10, further comprising applying the one or more of the rotational operation and the scaling operation to the sharp image.

14. The method of claim 10, further comprising applying, in reverse, the one or more of the rotational operation and the scaling operation to the blurred image.

15. A device-readable storage medium excluding signals per-se, the device-readable storage medium comprising instructions executable by a computing device to:

compute a series of trial images by applying a series of coordinate transforms to an operand image, the series of coordinate transforms differing with respect to one or more of a rotational operation and a scaling operation;

compute a series blur kernels corresponding to the series of trial images, each blur kernel relating a trial image from the series of trial images to a target image;

for each blur kernel in the series of blur kernels, evaluating blur-kernel sparseness to locate a sparsest blur kernel;

identify one or more of the rotational operation and the scaling operation of the coordinate transform relating the trial image corresponding to the sparsest blur kernel to the target image.

16. The storage medium of claim 15, wherein the target image is a blurred image.

17. The storage medium of claim 15, wherein the target image is a sharp image.

18. The storage medium device of claim 15, wherein the series of coordinate transforms spans a two-dimensional parameter space, the two-dimensional parameter space including a rotational parameter and a scaling parameter.

19. The storage medium of claim 15, wherein the series of coordinate transforms spans a four-dimensional parameter space, the four-dimensional parameter space including two rotational parameters and two scaling parameters.

20. The storage medium of claim 19, wherein the coordinate transform is based on an affine transformation.