Automatically suggesting regions for blur kernel estimation
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.
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.