ALIGNMENT OF SHARP AND BLURRED IMAGES BASED ON BLUR KERNEL SPARSENESS

• US 20100086232A1
• Filed: 10/03/2008
• Published: 04/08/2010
• 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
• Granted Patent: US 8,238,694 B2
• Time in Patent Office: Days
• 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:

locating a sparsest blur kernel in a parameter space, the sparsest blur kernel relating a select sharp image to a select blurred image, each of the select sharp image and the select blurred image chosen from the first and second images or computationally derived from the first image or the second image; and

applying an aligning operation to one or more of the first image and the second image, the aligning operation based on a location of the sparsest blur kernel in the parameter space.

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 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, wherein the series of blur kernels includes the sparsest blur kernel; 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 same 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;

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.

11. The method of claim 10, wherein locating the sparsest blur kernel in the series of blur kernels 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 locating the sparsest blur kernel in the series of blur kernels 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 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;

locate a sparsest blur kernel in the series of blur kernels;

identify one or more of a rotational operation and a 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 based on an affine transformation.