Filtering systems and methods for infrared image processing

• US 8,189,050 B1
• Filed: 08/13/2007
• Issued: 05/29/2012
• Est. Priority Date: 07/19/2006
• Status: Expired due to Fees

• Current Assignee: Teledyne FLIR LLC (Teledyne Technologies Incorporated)
• Original Assignee: FLIR Systems, Inc. (Teledyne Technologies Incorporated)
• Inventors: Hughes, Gary B., Högasten, Nicholas, Nagahawatte, Nuwan
• Primary Examiner(s): Wasel, Mohamed
• Application Number:
  US11/838,112
• Time in Patent Office: 1,751 Days
• Field of Search: 348162-164
• US Class Current: 348/164
• CPC Class Codes: H04N 17/002 for television cameras
• CPC Class Codes: H04N 23/20 for generating image signals from infrared radiation only
• CPC Class Codes: H04N 25/671 for non-uniformity detection or correction
• CPC Class Codes: H04N 25/68 applied to defects
• CPC Class Codes: H04N 5/33 Transforming infrared radiation cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20

Abstract

Systems and methods are disclosed herein to provide filtering techniques for image processing applications. For example, in accordance with an embodiment of the present invention, a Median-X filtering algorithm is disclosed that may be used for various infrared image processing applications.

Claims

1. A method of performing infrared image processing on infrared image data, the method comprising:

receiving one or more infrared image frames of the infrared image data;

calculating a median value of an X-shaped kernel surrounding each pixel of the infrared image frame; and

providing a low-frequency reference frame based on the calculating.

2. The method of claim 1, wherein the infrared image frames for the receiving are provided by an infrared sensor.

3. The method of claim 1, wherein the X-shaped kernel comprises a Median-Star kernel.

4. The method of claim 1, further comprising:

receiving a filter radius parameter for the X-shaped kernel;

receiving a number of passes parameter; and

repeating the calculating based on the number of passes parameter, with the low-frequency reference frame from the prior calculating used for the subsequent calculating.

5. The method of claim 4, wherein the method is performed within an infrared camera and the infrared image frames are stored as one-dimensional arrays, and wherein the method further comprises determining indices corresponding to the X-shaped kernel for the calculating.

6. The method of claim 1, further comprising:

determining a frame of pixel temporal averages based on the infrared image frames, wherein the calculating is performed on the pixel temporal averages;

subtracting the low-frequency reference frame from the frame of pixel temporal averages to provide a difference frame;

applying scalar values to the low-frequency reference frame to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the frame of pixel temporal averages; and

identifying bad pixels in the infrared image frame based on the performing of the pixel limit comparison.

7. The method of claim 6, further comprising determining a standard deviation based on the difference frame, wherein the scalar values are multiplied by the standard deviation, with the result used by the applying to set the upper and lower limits.

8. The method of claim 1, wherein the X-shaped kernel comprises a Median-Star kernel and wherein the method further comprises:

subtracting the low-frequency reference frames from the infrared image frames to provide difference frames;

determining a frame of pixel temporal averages based on the difference frames;

applying scalar values to the low-frequency reference frames to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the infrared image frames; and

identifying bad pixels in the infrared image frames based on the performing of the pixel limit comparison.

9. The method of claim 8, further comprising determining a standard deviation based on the frame of pixel temporal averages, wherein the scalar values are multiplied by the standard deviation, with the result used by the applying to set the upper and lower limits.

10. An infrared camera system comprising:

a processor;

a memory storing instructions for the processor to perform infrared image processing comprising;

performing a Median-X filtering algorithm on one or more infrared image frames; and

providing one or more low-frequency reference frames based on the performing of the Median-X filtering algorithm.

11. The infrared camera system of claim 10, further comprising an infrared sensor adapted to provide the infrared image frames, wherein the infrared camera system comprises an infrared camera or automated infrared camera test equipment and the infrared image frames are stored as one-dimensional arrays, and wherein the method further comprises determining indices for the Median-X filtering algorithm for the performing.

12. The infrared camera system of claim 10, wherein the median-X filtering algorithm uses a two-dimensional X-shaped kernel or a three-dimensional Median-Star kernel.

13. The infrared camera system of claim 10, wherein the infrared image processing comprises at least one of detail enhancement, flat-field correction, bad pixel detection, low pass filtering, or high pass filtering.

14. The infrared camera system of claim 10, wherein the instructions for the processor to perform infrared image processing further comprises:

determining a frame of pixel temporal averages based on the infrared image frames, wherein the performing is performed on the pixel temporal averages;

subtracting the low-frequency reference frame from the frame of pixel temporal averages to provide a difference frame;

applying scalar values to the low-frequency reference frame to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the frame of pixel temporal averages; and

identifying bad pixels in the infrared image frame based on the performing of the pixel limit comparison.

15. The infrared camera system of claim 10, wherein the Median-X filtering algorithm uses a Median-Star kernel and wherein the instructions for the processor to perform infrared image processing further comprises:

subtracting the low-frequency reference frames from the infrared image frames to provide difference frames;

determining a frame of pixel temporal averages based on the difference frames;

applying scalar values to the low-frequency reference frames to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the infrared image frames; and

identifying bad pixels in the infrared image frames based on the performing of the pixel limit comparison.

16. A non-transitory machine-readable storage medium containing information to provide a processor-based device with the capability to perform a method of infrared image processing, the method comprising:

performing a Median-X filtering algorithm on one or more infrared image frames; and

providing one or more low-frequency reference frames based on the performing of the Median-X filtering algorithm.

17. The method of claim 16, wherein the median-X filtering algorithm uses a two-dimensional X-shaped kernel or a three-dimensional Median-Star kernel.

18. The method of claim 16, wherein the infrared image processing comprises at least one of detail enhancement, flat-field correction, bad pixel detection, low pass filtering, or high pass filtering.

19. The method of claim 16, wherein the method further comprises:

determining a frame of pixel temporal averages based on the infrared image frames, wherein the performing is performed on the pixel temporal averages;

subtracting the low-frequency reference frame from the frame of pixel temporal averages to provide a difference frame;

applying scalar values to the low-frequency reference frame to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the frame of pixel temporal averages; and

identifying bad pixels in the infrared image frame based on the performing of the pixel limit comparison.

20. The method of claim 19, wherein the method further comprises determining a standard deviation based on the difference frame, wherein the scalar values are multiplied by the standard deviation, with the result used by the applying to set the upper and lower limits.

21. The method of claim 16, wherein the Median-X filtering algorithm uses a Median-Star kernel and wherein the method further comprises:

subtracting the low-frequency reference frames from the infrared image frames to provide difference frames;

determining a frame of pixel temporal averages based on the difference frames;

applying scalar values to the low-frequency reference frames to set upper and lower limits;

performing a pixel limit comparison based on the upper and lower limits and the infrared image frames; and

identifying bad pixels in the infrared image frames based on the performing of the pixel limit comparison.

22. The method of claim 21, wherein the method further comprises determining a standard deviation based on the frame of pixel temporal averages, wherein the scalar values are multiplied by the standard deviation, with the result used by the applying to set the upper and lower limits.

23. The method of claim 16, wherein the infrared image frames are stored as one-dimensional arrays, and wherein the method further comprises determining indices for the Median-X filtering algorithm for the performing.