Motion blur modeling for image formation

• US 8,860,824 B2
• Filed: 08/06/2010
• Issued: 10/14/2014
• Est. Priority Date: 08/06/2010
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Jelinek, Jan
• Primary Examiner(s): Ye, Lin
• Assistant Examiner(s): YODER III, CHRISS S
• Application Number:
  US12/852,299
• Publication Number:
  US 20120033096A1
• Time in Patent Office: 1,530 Days
• Field of Search: 348/208.12, 348/208.13, 348/208.14, 348/208.4, 348/362, 348/363, 348/367, 348/208.6, 348/222.1
• US Class Current: 348/208.14
• CPC Class Codes: G06T 2207/20201 Motion blur correction
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening

Abstract

The present disclosure includes motion blur modeling methods and systems for image formation. One motion blur modeling method for image formation includes obtaining image data utilized to form a particular image taken by a camera of a subject, obtaining velocity vector data for the subject at the time the image was taken, defining a convolution kernel for use formation of the particular image and based upon the velocity vector data, and applying the convolution kernel to the image data to produce a de-blurred set of image data utilized to form the particular image.

Claims

1. A motion blur modeling method for image formation, comprising:

obtaining image data utilized to form a particular image taken by a camera of a subject;

obtaining, from a source curve, velocity vector data for the subject at the time the image was taken by identifying a residence time of a point along the source curve, wherein;

the residence time of the point along the source curve is an amount of time the point spends within a particular pixel; and

the source curve is a locus of a plurality of points in an image plane, corresponding to a single pixel of the image, wherein at least one of the plurality of points has a trajectory that passes over another one of the plurality of points in the single pixel;

defining a convolution kernel for use formation of the particular image and based upon the velocity vector data; and

applying the convolution kernel to the image data to produce a de-blurred set of image data utilized to form the particular image.

2. The method of claim 1, wherein the camera is an array of sensors.

3. The method of claim 2, wherein a unique source curve is associated with each point in the sensor array.

4. The method of claim 1, wherein defining a convolution kernel for use formation of the particular image and based upon the velocity vector data includes a time component that is the time to an anchor point.

5. An image formation system, comprising:

a camera for capturing image data utilized to form a particular image taken by a camera of a subject;

a velocity vector estimator for identifying a velocity vector and generating, from a source curve, velocity vector data for the subject at the time the image was taken by identifying a residence time of a point along the source curve, wherein;

the residence time of the point along the source curve is an amount of time the point spends within a particular pixel; and

the source curve is a locus of a plurality of points in an image plane, corresponding to a single pixel of the image, wherein at least one of the plurality of points has a trajectory that passes over another one of the plurality of points in the single pixel;

memory for the storage of at least one of image data and velocity vector data;

an image processor for defining a convolution kernel for use in formation of the particular image and based upon the velocity vector data and applying the convolution kernel to the image data to produce a de-blurred set of image data utilized to form the particular image.

6. The image formation system of claim 5, wherein the camera is a shutter flutter camera.

7. The image formation system of claim 5, wherein the camera is a digital camera.

8. The image formation system of claim 5, wherein the velocity vector estimator is a motion sensor.

9. The image formation system of claim 5, the velocity vector estimator is a second camera.

10. The image formation system of claim 5, wherein the memory and image processor are in a computing device.

11. The image formation system of claim 5, wherein the memory and image processor are in communication via a network.

12. A non-transitory computer readable medium having computer-executable instructions stored thereon for execution by a processor to provide a motion blur modeling method for image formation, the method including:

obtaining image data utilized to form a particular image taken by a camera of a subject;

obtaining, from a source curve, velocity vector data for the subject at the time the image was taken by identifying a residence time of a point along the source curve, wherein;

the residence time of the point along the source curve is an amount of time the point spends within a particular pixel; and

the source curve is a locus of a plurality of points in an image plane, corresponding to a single pixel of the image, wherein at least one of the plurality of points has a trajectory that passes over another one of the plurality of points in the single pixel;

defining a convolution kernel for use formation of the particular image and based upon the velocity vector data; and

applying the convolution kernel to the image data to produce a de-blurred set of image data utilized to form the particular image.

13. The method of claim 12, wherein defining a convolution kernel for use formation of the particular image and based upon the velocity vector data includes a time component.

14. The method of claim 12, wherein defining a convolution kernel for use formation of the particular image and based upon the velocity vector data wherein the velocity vector data indicates the velocity is changing over time.

15. The method of claim 12, wherein defining a convolution kernel for use formation of the particular image and based upon the velocity vector data utilizes two dimensional velocity vector data.