Mechanism for deriving noise-reduced estimates of color signal parameters from multiple color/luminance image sensor outputs

• US 5,264,924 A
• Filed: 12/18/1989
• Issued: 11/23/1993
• Est. Priority Date: 12/18/1989
• Status: Expired due to Term

• Current Assignee: Eastman Kodak Company
• Original Assignee: Eastman Kodak Company
• Inventors: Cok, David R.
• Primary Examiner(s): CHIN, TOMMY P
• Application Number:
  US07/451,710
• Time in Patent Office: 1,436 Days
• Field of Search: 358/52, 358/41, 358/36, 358/37, 358/44
• US Class Current: 348/624
• CPC Class Codes: H04N 1/58 Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction H04N1/62 takes precedence
• CPC Class Codes: H04N 23/84 for processing colour signals
• CPC Class Codes: H04N 25/60 Noise processing, e.g. detecting, correcting, reducing or removing noise

Abstract

Color and luminance measurement signals produced by the multiple sensors of a multiband (e.g. RGB color) image signal processing system are processed to reduce noise. For images such as those obtained from a multi-dye film, the luminance component of the output signal from the luminance sensor may be expressed as a function of the color components of the color sensor output signals. A first signal, representative of the constructed low frequency luminance signal, is produced as a linear function of the color sensor output signals, employing signal weighting coefficients derivable from intrinsic characteristics of the scanned image, such as the respective dye densities of a three layer color film. The luminance measurement signal derived from the luminance sensor is subtracted from this first signal, to produce a second signal, which is multiplied by a set of respective scaling factors, which are then subtracted from each color sensor output, so as to derive a set of noise-reduced estimates of the color components of each of the color sensor output signals. Each scaling factor is defined in accordance with the noise variances associated with the operation of the color image sensors and the luminance image sensor, and is proportional to a product of a corresponding one of the coefficients and the ratio of the noise variance associated with the operation of a respective color image sensor to a summation of the noise variances associated with the operation of each of the color sensors and the luminance sensor.

Claims

1. For use with a multiple band image sensor system having a plurality of first image sensors, each of which produces a respective first sensor output signal that contains an information component representative of a respective characteristic of an image and a noise component associated with the operation of a sensor through which its output signal is produced, and a further image sensor that produces a further sensor output signal containing an information component representative of a further characteristic of said image, and which is expressible as a function of the information components of said first sensor output signals, and a noise component associated with the operation of said further image sensor, a method of processing said first and further sensor output signals to reduce the noise components in said first sensor output signals and thereby derive noise-reduced estimates of each of the information components within said first sensor output signals comprising the steps of:

(a) generating a first signal, representative of said further image sensor signal, as a function of said first sensor output signals;

(b) generating a second signal representative of a prescribed relationship between said first signal and said further sensor output signal; and

(c) combining the first sensor output signals produced by said first image sensors with said second signal generated in step (b), to obtain said noise-reduced estimates of the information components of each of said first sensor output signals.

2. A method according to claim 1, wherein step (b) comprises generating said second signal as the difference between said first signal and said further sensor output signal.

3. A method according to claim 2, wherein step (c) comprises subtracting a fraction of said second signal from each of said first sensor output signals to thereby obtain said noise-reduced estimates of the information components of each of said first sensor output signals.

4. A method according to claim 1, wherein step (c) includes generating a plurality of third signals representative of the noise variances associated with the operation of said first image sensors and said further image sensor, and combining the first sensor output signals produced by said first image sensors with the second signal generated in step (b) and said plurality of third signals, to thereby obtain said noise-reduced estimates of the information components of each of said first sensor output signals.

5. A method according to claim 4, wherein a respective one of said third signals is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.

6. A method according to claim 1, wherein said plurality of first image sensors produce first sensor output signals associated with respectively different color components of said image, and wherein step (a) comprises generating said first signal as a linear function of the respectively different color representative first sensor output signals.

7. A method according to claim 6, wherein said further image sensor produces said further sensor output signal in accordance with a luminance component of said image, and wherein step (b) comprises generating said second signal as the difference between said first signal and said luminance component representative further sensor output signal.

8. A method according to claim 1, wherein said plurality of first image sensors produce said first sensor output signals R, G and B, respectively associated with red, green and blue color components of an image, and wherein step (a) comprises generating said first signal S1 as a linear function of the respective red, green and blue color representative first sensor output signals, in accordance with the relationship:

space="preserve" listing-type="equation">S1=aR+bG+cB,

where a, b, and c are prescribed coefficients.

9. A method according to claim 8, wherein said further image sensor produces said further sensor output signal L in accordance with a luminance component of said image, and wherein step (b) comprises generating said second signal S2 as the difference between said first signal S1 and said luminance component representative further sensor output signal L.

10. A method according to claim 9, wherein step (c) includes generating a plurality of third signals S3, a respective one of which is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor, and combining the first sensor output signals R, G and B produces by said first image sensors with the second signal S2 generated in step (b) and said plurality of third signals, to thereby obtain said noise-reduced estimates of the information components of each of said first sensor output signals.

11. A method according to claim 10, wherein a respective third signal, associated with one of said color components, is proportional to a product of a corresponding one of said coefficients and the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.

12. For use with a multiple band image sensor system having a plurality of first image sensors, each of which produces a respective first sensor output signal that contains an information component representative of a respective characteristic of an image and a noise component associated with the operation of the sensor through which its output signal is produced, and a further image sensor that produces a further sensor output signal containing an information component that is representative of a further characteristic of said image, and is expressible as a function of the information components of said first sensor output signals, and a noise component associated with the operation of said further image sensor, a method of processing said first and further sensor output signals to reduce the noise components in said first sensor output signals and thereby derive noise-reduced estimates of each of the information components within said first sensor output signals, said method comprising the steps of:

(a) generating a first signal, representative of the information component within said further image sensor signal, as a function of said first sensor output signals;

(b) generating a plurality of second signals representative of the noise variances associated with the operation of said first image sensors and said further image sensor; and

(c) combining the first and further sensor output signals produced by said first and further image sensors, respectively, with said first signal generated in step (a) and said plurality of second signals generated in step (b), to obtain said noise-reduced estimates of the information components of each of the information components within said first sensor output signals.

13. A method according to claim 12, wherein a respective one of said second signals is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.

14. A method according to claim 13, wherein said plurality of first image sensors produce said first sensor output signals associated with respectively different color components of said image, and wherein step (a) comprises generating said first signal as a linear function of the respectively different color representative first sensor output signals.

15. A method according to claim 14, wherein said further image sensor produces said further sensor output signal in accordance with a luminance component of said image, and wherein step (c) comprises generating a third signal as the difference between said first signal and said luminance component representative further sensor output signal and subtracting from said first sensor output signals respective combinations of said second and third signals.

16. A method according to claim 15, wherein said plurality of first image sensors produce said first sensor output signals R, G and B, respectively associated with red, green and blue color components of an image, and wherein step (a) comprises generating said first signal S1 as a linear function of the respective red, green and blue color representative first sensor output signals, in accordance with the relationship:

space="preserve" listing-type="equation">S1=aR+bG+cB,

where a, b, and c are prescribed coefficients.

17. A method according to claim 16, wherein said further image sensor produces said further sensor output signal L in accordance with a luminance component of said image, and wherein step (c) comprises generating a third signal as the difference between said first signal S1 and said luminance component representative further sensor output signal L.

18. A method according to claim 17, wherein step (c) includes generating a plurality of second signals, a respective one of which is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor, and combining the first sensor output signals R, G and B produced by said first image sensors with said third signals and said plurality of second signals, to thereby obtain said noise-reduced estimates of the information components of each of said first sensor output signals.

19. A method according to claim 18, wherein a respective one of said second signals, associated with one of said color components, is proportional to a product of a corresponding one of said coefficients and the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.

20. For use with a multiple color image sensor system having a plurality of first image sensors, each of which produces a respective first sensor output signal that contains a respective color representative component of an image and a noise component associated with the operation of a sensor through which its output signal is produced, and a further image sensor that produces a further sensor output signal containing a luminance representative component of said image, and which is expressible as a function of color components of said first sensor output signals, and a noise component associated with the operation of said further image sensor, a method of processing said first and further sensor output signals to reduce the noise components in said first sensor output signals and thereby derive noise-reduced estimates of each of the color components of said first sensor output signals comprising the steps of:

(a) generating a first signal, representative of said further image sensor signal, as a function of said first sensor output signals;

(b) generating a second signal representative of a prescribed relationship between said first signal and said further sensor output signal; and

(c) combining the first sensor output signals produced by said first image sensors with said second signal generated in step (b), to obtain said noise-reduced estimates of the color components of each of said first sensor output signals.

21. A method according to claim 20, wherein step (b) comprises generating said second signal as the difference between said first signal and said further sensor output signal, and wherein step (c) comprises subtracting a fraction of said second signal from each of said first sensor output signals to thereby obtain said noise-reduced estimates of the color components of each of said first sensor output signals.

22. A method according to claim 20, wherein step (c) includes generating a plurality of third signals representative of the noise variances associated with the operation of said first image sensors and said further image sensor, and combining the first sensor output signals produced by said first image sensors with the second signal generated in step (b) and said plurality of third signals, to thereby obtain said noise-reduced estimates of the color components of each of said first sensor output signals.

23. A method according to claim 22, wherein a respective one of said third signals is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.

24. A method according to claim 20, wherein step (a) comprises generating said first signal as a linear function of the respectively different color representative first sensor output signals, and wherein step (b) comprises generating said second signal as the difference between said first signal and said luminance component representative further sensor output signal.

25. A method according to claim 20, wherein said plurality of first image sensors produce said first sensor output signals R, G and B, respectively associated with red, green and blue color components of an image, and wherein step (a) comprises generating said first signal S1 as a linear function of the respective red, green and blue color representative first sensor output signals, in accordance with the relationship:

space="preserve" listing-type="equation">S1=aR+bG+cB,

where a, b, and c are prescribed coefficients, and wherein step (b) comprises generating said second signal S2 as the difference between said first signal S1 and said luminance component representative further sensor output signal.

26. A method according to claim 25, wherein step (c) includes generating a plurality of third signals S3, a respective one of which is representative of the ratio of the noise variance associated with the operation of a respective first image sensor to a summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor, and combining the first sensor output signals R, G and B produced by said first image sensors with the second signal S2 generated in step (b) and said plurality of third signals, to thereby obtain said noise-reduced estimates of the color components of each of said first sensor output signals.

27. A method according to claim 26, wherein a respective one of said third signals, associated with one of said color components, is proportional to a product of a corresponding one of said coefficients and the ratio of the noise variance associated with the operation of a respective first image sensor to said summation of the noise variances associated with the operation of said plurality of first image sensors and said further image sensor.