Method of non-uniformity compensation (NUC) of an imager

• US 7,881,495 B2
• Filed: 10/28/2008
• Issued: 02/01/2011
• Est. Priority Date: 09/15/2004
• Status: Expired due to Fees

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Williams, Darin S., Kitchen, Edward N.
• Primary Examiner(s): Bhatnagar; Anand
• Application Number:
  US12/259,659
• Publication Number:
  US 20090103776A1
• Time in Patent Office: 826 Days
• Field of Search: 382/100, 382/103, 382/106, 382/107, 382/181, 382/190, 382/195, 382/199, 382/201, 382/203, 342/52, 342/53, 342/141, 250/316.1, 250/30, 250/336.1, 250/338.1, 250/341.8, 250/342, 250/495.1, 702/127, 702/135, 702/134, 348/169
• US Class Current: 382/103
• CPC Class Codes: F41G 7/007 Preparatory measures taken before the launching of the guided missiles
• CPC Class Codes: F41G 7/2226 comparing the observed data with stored target data, e.g. target configuration data
• CPC Class Codes: F41G 7/2253 Passive homing systems, i.e. comprising a receiver and do not requiring an active illumination of the target
• CPC Class Codes: F41G 7/2293 using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 3/7803 Means for monitoring or calibrating
• CPC Class Codes: H04N 25/674 based on the scene itself, e.g. defocusing
• 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

The present invention provides for simple and streamlined boresight correlation of FLIR-to-missile video. Boresight correlation is performed with un-NUCed missile video, which allows boresight correlation and NUC to be performed simultaneously thereby reducing the time required to acquire a target and fire the missile. The current approach uses the motion of the missile seeker for NUCing to produce spatial gradient filtering in the missile image by differencing images as the seeker moves. This compensates DC non-uniformities in the image. A FLIR image is processed with a matching displace and subtract spatial filter constructed based on the tracked scene motion. The FLIR image is resampled to match the missile image resolution, and the two images are preprocessed and correlated using conventional methods. Improved NUC is provided by cross-referencing multiple measurements of each area of the scene as viewed by different pixels in the imager. This approach is based on the simple yet novel premise that every pixel in the array that looks at the same thing should see the same thing. As a result, the NUC terms adapt to non-uniformities in the imager and not the scene.

Claims

1. A method of non-uniformity compensation (NUC) of the detection elements of a pixilated imager, comprising:

registering a temporal sequence of input images from the imager having different fields of view (FOVs) to create a registered average image as an estimate of an input scene;

backward mapping the estimated input scene to the different FOVs to create an estimate input image for each input image;

comparing each said estimate input image to its associated original input image to estimate pixel-by-pixel response non-uniformity compensation (NUC) terms for the imager; and

applying the NUC terms to the images to generate compensated images.

2. The method of claim 1, wherein the estimate of the input scene is created by a forward mapping of,

adding the registered input images together to create a registered sum image;

accumulating the number of input images that contribute to each pixel in the scene in a registered count image;

dividing the registered sum image by the registered count image to form the registered average image.

3. The method of claim 2, further comprising keeping track of a variability of the response of the imager pixels in the forward mapping to update the registered sum image, wherein imager pixels that provide the most consistent response contribute most heavily to the registered sum and registered count images, hence the estimate of the input scene

4. The method of claim 2, wherein the registered average image is maintained at a higher resolution than the input image.

5. The method of claim 4, wherein a nearest neighbor mapping is used to expand the number of pixels in the FOV of the input image into the registered average image, phase variation in the input images causing aliased frequency components from the mapping to cancel while artifacts that are consistent with beyond-Nyquist frequencies in the input image add constructively and are preserved.

6. The method or claim 2, wherein said backward mapping comprises convolving a FOV motion trace against the registered average image to form an ideal stacked average that is compared to a stacked average of the input images to form a composite unexplained difference image that is output as the NUC error terms to update the NUC terms, said NUC error terms being correlated again the FOV motion trace and added to the registered sum image to update the registered average image for another iteration on the NUC error terms.

7. The method of claim 1, wherein the estimated input scene is backward mapped to each input image by cutting out a frame in the estimated input scene that corresponds to that input image'"'"'s field of view.

8. The method of claim 7, further comprising keeping track of a variability of the scene pixels in the backward mapping to update the estimated NUC terms, wherein those areas of the estimate of the input scene which are temporally stable and locally flat contribute most heavily to the estimate of the NUC term.

9. The method of claim 1, wherein the NUC terms are estimated by,

differencing respective pairs of input images and estimated input images to form a sequence of unexplained difference images;

performing an Nth order correlation on said unexplained difference images to form NUC error terms; and

combining the NUC error terms with the NUC terms to update the NUC terms.

10. The method of claim 1, wherein the steps are repeated on the compensated images to improve the estimate of the NUC terms.

11. The method of claim 1, wherein the temporal sequence of input images is captured by an infrared (IR) imager.

12. A method of non-uniformity compensation (NUC) for a pixilated imager, comprising:

capturing a temporal sequence of input images from the imager having different fields of view (FOVs);

compensating each input image with apriori NUC terms if they exist to form compensated images;

tracking a FOV motion across the scene;

registering and adding the input images together to create a registered sum image;

accumulating the number of input images that contribute to each pixel in the scene in a registered count image;

dividing the registered sum image by the registered count image to form a registered average image;

using the FOV motion to backward map the registered average image to the different FOVs to create an estimated input image for each input image;

differencing respective pairs of compensated images and estimated input images to form a sequence of unexplained difference images;

performing an Nth order correlation on said unexplained difference images to form NUC error terms; and

combining the NUC error terms with the NUC terms from the previous iteration to update the NUC terms.

13. The method of claim 12, further comprising keeping track of a variability of the response of the imager pixels to update the registered sum and registered count images and a variability of scene pixels to weight the Nth order correlation.

14. A method of non-uniformity compensation (NUC) for a pixilated imager, comprising:

capturing a temporal sequence of input images from the IR imager having different fields of view (FOVs);

compensating each input image with apriori NUC terms to form compensated images;

tracking a FOV motion across the scene;

registering and adding the input images together to create a registered sum image;

accumulating the number of input images that contribute to each pixel in the scene in a registered count image;

dividing the registered sum image by the registered count image to form a registered average image;

convolving the FOV motion against the registered average image to form an ideal stacked average;

comparing the ideal stacked average to a stacked average of the compensated images to form a composite unexplained difference image that is output as the NUC error terms to update the NUC terms;

correlating said NUC error terms against the FOV motion trace to form a registered sum error; and

adding the registered sum error to the registered sum image to update the registered average image for another iteration on the NUC error terms.