OPTIMUM NON-UNIFORMITY CORRECTION FOR IMAGING SENSORS

• US 20070103742A1
• Filed: 01/13/2003
• Published: 05/10/2007
• Est. Priority Date: 01/13/2003
• Status: Active Grant

• Current Assignee: BAE Systems Information and Electronic Systems Integration Incorporated Reconnaissance and Surveillance Systems
• Original Assignee: BAE Systems Information and Electronic Systems Integration Incorporated (BAE Systems Plc), Reconnaissance and Surveillance Systems
• Inventors: Ernandes, Kenneth, Kleehammer, Robert
• Granted Patent: US 7,230,741 B2
• Time in Patent Office: Days
• US Class Current: 358/504
• CPC Class Codes: H04N 1/40006 Compensating for the effects of ageing, i.e. changes over time
• CPC Class Codes: H04N 1/401 Compensating positionally unequal response of the pick-up or reproducing head H04N1/403 takes precedence
• CPC Class Codes: H04N 25/671 for non-uniformity detection or correction

Abstract

The present invention relates to a method of generating intensity measurement correction coefficients for a pixel in a focal plane array of an optical sensor, wherein the correction coefficients are used to correct an intensity measurement generated by the pixel responsive to illumination. The method includes the steps of accessing past values of the correction coefficients, receiving a pixel intensity measurement indicative of illumination characterized by a reference energy density function, generating a modeled noise signal, and generating present values of the correction coefficients. The present values of the correction coefficients are generated based on the past values of the correction coefficients, the pixel intensity measurement, the modeled noise signal, and the reference energy density function.

Claims

1. A method of generating intensity measurement correction coefficients for a pixel in a focal plane array of an optical sensor, comprising the steps of:

(a) accessing past values of the correction coefficients;

(b) receiving a pixel intensity measurement from the pixel indicative of illumination characterized by a reference energy density function;

(c) generating a modeled noise signal; and

(d) generating present values of the correction coefficients, as a function of

the past values of the correction coefficients,

the pixel intensity measurement,

the modeled noise signal, and

the reference energy density function.

2. The method of claim 1, wherein the past values of the correction coefficients are initial predetermined values.

3. The method of claim 1, wherein the correction coefficients include a gain coefficient (G) and an offset coefficient (O).

4. The method of claim 3, wherein the gain coefficient (G) and offset coefficient (O) are used to correct a pixel intensity measurement I_READ generated by the pixel responsive to scene illumination, according to:

I_READ=(I_C−

O)/G, where I_C represents a corrected pixel intensity measurement.

5. The method of claim 1, further comprising

(e) repeating steps (a) through (d) at least twice

using different reference energy density functions in each iteration using steps (a) through (d), and

using present values of the correction coefficients generated in a past iteration through steps (a) through (d) as the past values of the correction coefficients in the next iteration through steps (a) through (d).

6. The method of claim 5, further comprising:

(f) repeating steps (a) through (e) for at least another pixel.

7. The method of claim 1, wherein the step (c) comprises generating the at least one noise signal as a modeled noise signal in accordance with a sensor random noise model, the sensor random noise model including at least one of shot noise, detector drift, quantization error noise, focal plane readout noise, and random noise.

8. The method of claim 1, wherein step (d) comprises generating the present values of the correction coefficients using a Kalman filter process.

9. The method of claim 8, wherein the Kalman filter process of step (d) comprises:

(d)(i) generating a process noise signal representative of variances of the correction coefficients; and

(d)(ii) estimating a covariance matrix update for an update covariance matrix for the correction coefficients, based on

(1) the process noise signal, and

(2) a past value of the update covariance matrix.

10. The method of claim 9, wherein step (d)(i) comprises generating the process noise signal based on

(1) the past values of the correction coefficients, and

(2) the reference energy density function.

11. The method of claim 9, wherein the Kalman filter process of step (d) further comprises:

(d)(iii) computing a Kalman gain matrix for the correction coefficients, as a function of

(1) the estimated covariance matrix update,

(2) the pixel intensity measurement, and

(3) the modeled noise signal.

12. The method of claim 11, wherein the Kalman filter process of step (d) further comprises:

(d)(iv) generating the present values of the correction coefficients, based on

(1) the Kalman gain matrix,

(2) the past values of the correction coefficients,

(3) the pixel intensity measurement, and

(4) the reference energy density function.

13. The method of claim 12, wherein the Kalman filter process of step (d) further comprises:

(d)(v) generating an updated covariance matrix based on

(1) the Kalman gain matrix,

(2) the pixel intensity measurement, and

(3) the estimated covariance matrix update.

14. The method of claim 13, wherein the reference energy density function is a black body energy density function.

15. A system configured to generate intensity measurement correction coefficients for a pixel in a focal plane array of an optical sensor, the pixel being configured to generate a pixel intensity measurement in response to reference illumination characterized by a reference energy density function, comprising:

a memory for storing past values of the correction coefficients;

a modeled noise generator for generating a modeled noise signal; and

a Kalman filter for generating present values of the correction coefficients, based on

the past values of the correction coefficients,

the pixel intensity measurement,

the modeled noise signal, and

the reference energy density function.

16. The system of claim 15, wherein the past values of the correction coefficients are initial predetermined values.

17. The system of claim 15, wherein the correction coefficients include a gain coefficient (G) and an offset coefficient (O).

18. The system of claim 17, further comprising a scene corrector, wherein the scene corrector uses the gain coefficient (G) and offset coefficient (O) to generate a corrected pixel intensity measurement (I_C) from a pixel intensity measurement I_READ according to:

I_READ=(I_C−

O)/G

19. The system of claim 15, wherein the modeled noise generator is configured to generate the modeled noise signal in accordance with a sensor random noise model, the sensor random noise model including at least one of shot noise, quantization error noise, focal plane readout noise, and random noise.

20. The system of claim 15, wherein the Kalman filter uses a Kalman filter process to generate the present values of the correction coefficients.

21. The system of claim 20, wherein the Kalman filter further comprises:

a process noise generator for generating a process noise signal representative of variances of the correction coefficients;

a covariance matrix updater for generating an update covariance matrix of the correction coefficients; and

a covariance matrix update estimator for estimating a covariance matrix update for an update covariance matrix of the correction coefficients, based on

the process noise signal, and

a past value of the update covariance matrix.

22. The system of claim 21, wherein the process noise generator generates the process noise signal based on

the past values of the correction coefficients, and

the reference energy density function.

23. The system of claim 21, wherein the Kalman filter further comprises:

a Kalman gain matrix updater for computing a Kalman gain matrix for the correction coefficients, based on

the estimated covariance matrix update,

the pixel intensity measurement, and

the modeled noise signal, wherein the modeled noise signal is generated by the modeled noise generator.

24. The system of claim 23, wherein the Kalman filter is configured to generate the present values of the correction coefficients, based on

the Kalman gain matrix,

the past values of the correction coefficients,

the pixel intensity measurement, and

the reference energy density function.

25. The system of claim 24, wherein the Kalman filter is configured to generate an updated covariance matrix based on

the Kalman gain matrix,

the pixel intensity measurement, and

the estimated covariance matrix update.

26. The system of claim 15, wherein the reference energy density function is a black body energy density function.