Automatically suggesting regions for blur kernel estimation

Patent metadata

Field	Value
Publication number	US-9349165-B2
Application number	US-201314061131-A
Country	US
Kind code	B2
Filing date	Oct 23, 2013
Priority date	Oct 23, 2013
Publication date	May 24, 2016
Grant date	May 24, 2016

Abstract

A computer-implemented method and apparatus are described for automatically selecting a region in a blurred image for blur kernel estimation. The method may include accessing a blurred image and defining a size for each of a plurality of regions in the image. Thereafter, metrics for at least two of the plurality of regions are determined, wherein the metrics are based on a number of edge orientations within each region. A region is selected from the plurality of regions based on the determined metrics, and a blur kernel for deblurring the blurred image is then estimated for the selected region. The blurred image is then deblurred using the blur kernel.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
accessing a blurred image;
defining a size for each of a plurality of regions in the blurred image;
determining metrics for at least two of the plurality of regions, the metrics being based on a number of edge orientations within each region, a gradient magnitude of pixels within the region, and a weight associated with the pixels within the region;
selecting a region from the plurality of regions based on the determined metrics;
based on the selected region, estimating a blur kernel for deblurring the blurred image; and
deblurring the blurred image using the blur kernel to produce a deblurred image.

2. The method of claim 1 , wherein selecting the region is based on the number of edge orientations of the region exceeding a threshold value.

3. The method of claim 2 , wherein the metrics are further based on a usefulness factor, the usefulness factor determined from the number of edge orientations and an image gradient.

4. The method of claim 3 , wherein the metrics are further based on a number of over-exposed or under-exposed pixels, an influence of the over-exposed or under-exposed pixels being weighted.

5. The method of claim 1 , wherein the metrics are further based on a location weight associated with a location of the region within the blurred image.

6. The method of claim 5 , wherein the location weight is a pixel weight function determined at least partially by a distance between a center pixel in a region of the plurality of regions and a center of the blurred image.

7. The method of claim 6 , further comprising:
receiving a user input identifying a user defined location at which to estimate the blur kernel in the blurred image; and
modifying the location weight based on the user input.

8. The method of claim 1 , wherein the blurred image is downsampled with respect to the size of the blur kernel.

9. The method of claim 1 , further comprising automatically, without user input, determining the size of the blur kernel.

10. The method of claim 1 , the method further comprising:
modifying a size of each of the plurality of regions;
determining metrics for each of the plurality of regions having a modified size; and
selecting a region having the modified size that is associated with a metric that satisfies a threshold metric for estimating the blur kernel.

11. An image deblur system, comprising:
one or more processors;
memory, coupled with the one or more processors, having instructions stored thereon, the instructions, when executed by the one or more processors, to cause the image deblur system to:
access a blurred image;
determine metrics for at least two regions of a plurality of regions, the metrics being based on a number of edge orientations within each region, a gradient magnitude of pixels within the region, and a weight associated with the pixels within the region;
to select a region of the plurality of regions based on the determined metrics; and
to estimate a blur kernel for deblurring the blurred image, wherein the blur kernel is based on the selected region.

12. The system of claim 11 , wherein to select the region is based the number of edge orientations of the region exceeding a threshold value.

13. The system of claim 11 , wherein the metrics are further based on a usefulness factor, the usefulness factor determined from the number of edge orientations and an image gradient.

14. The system of claim 13 , wherein the metrics are further based on a number of over-exposed or under-exposed pixels, an influence of the over-exposed or under-exposed pixels being weighted.

15. The system of claim 11 , wherein the metrics are further based on a location weight associated with a location of the region within the blurred image.

16. The system of claim 11 , wherein the instructions further cause the system is configured to:
modify a size of each of the plurality of regions;
determine metrics for each of the plurality of regions having a modified size; and
select a region having the modified size that is associated with a metric that satisfies a threshold metric for estimating the blur kernel.

17. A computer-readable storage device including instructions which, when executed by a computer, cause the computer to perform operations comprising:
accessing a blurred image;
defining a size for each of a plurality of regions in the blurred image;
determining metrics for at least two regions of the plurality of regions, the metrics based on a number of edge orientations within each region, a gradient magnitude of pixels within each region, and a weight associated with the pixels within each region;
selecting a region from the plurality of regions based on the determined metrics;
based on the selected region, estimating a blur kernel for deblurring the blurred image; and
deblurring the blurred image using the blur kernel to produce a deblurred image.

18. The computer-readable storage device of claim 17 , the region is selected based on the number of edge orientations of the region exceeding a threshold value.

Assignees

• Adobe Systems Inc

Inventors

• Cho Sunghyun
• Wang Jue
• Chien Jen-Chan
• Feng Dong

Classifications

• G06T11/10

  Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) · CPC title

• G06T5/003 Primary

  Physics · mapped topic

• G06T2207/20192

  Edge enhancement; Edge preservation · CPC title

• G06T5/73 Primary

  Deblurring; Sharpening · CPC title

• G06T5/20

  using local operators · CPC title