Reducing noise in digital images

• US 8,630,508 B2
• Filed: 03/01/2012
• Issued: 01/14/2014
• Est. Priority Date: 08/07/2006
• Status: Expired due to Fees

• Current Assignee: STRATA Skin Sciences, Inc.
• Original Assignee: Mela Sciences, Inc.
• Inventors: Gutkowicz-Krusin, Dina, Kabelev, Nikolai
• Primary Examiner(s): Bali, Vikkram
• Assistant Examiner(s): TORRES, JOSE
• Application Number:
  US13/409,397
• Publication Number:
  US 20120162487A1
• Time in Patent Office: 684 Days
• Field of Search: 382/128, 382/254, 382/260, 382/275, 382/278, 348/241, 348/243
• US Class Current: 382/275
• CPC Class Codes: G06T 2207/20008 Globally adaptive
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: H04N 25/63 applied to dark current
• CPC Class Codes: H04N 25/677 for reducing the column or line fixed pattern noise
• CPC Class Codes: H04N 5/21 Circuitry for suppressing or minimising disturbance, e.g. moiré or halo

Abstract

A target digital image is received from an image sensor. The image is contaminated by noise of unknown magnitude that is represented by a reference digital image. A process is applied that uses statistical analysis of the target digital image and of the reference digital image to estimate a magnitude of the noise for at least some pixels of the target digital image.

Claims

1. A method comprising:

receiving, from an image sensor, a target digital image T contaminated by noise of unknown magnitude A that is represented by a reference digital image; and

applying a process that uses a variance minimization analysis with respect to the unknown magnitude A associated with the target digital image and the reference digital image, to determine the magnitude of the noise for at least some pixels of the target digital image,

wherein the determination using the variance minimization analysis is mathematically and statistically equivalent to a determination of the magnitude of the noise made from a decorrelation condition;

Correlation(T−

A*{the reference digital image},{the reference digital image})=0.

2. The method of claim 1 in which the determination is made over pixels in some regions of the target digital image T and the reference digital image to estimate a magnitude of the noise for each of at least some pixels of the target digital image T.

3. The method of claim 2 also comprising reducing noise in every pixel of the target digital image T using the estimated noise magnitudes for all pixels of the target digital image T to produce a noise-reduced target digital image.

4. The method of claim 1 in which the sensor comprises a CMOS sensor.

5. The method of claim 1 in which the sensor comprises a CCD sensor.

6. The method of claim 1 in which the noise comprises dark current noise.

7. The method of claim 1 also comprising subtracting fixed vertical patterns, pixel by pixel, from the target digital image T, to produce a vertical-pattern-corrected target digital image.

8. The method of claim 7 also comprising applying a dark current removal function to the vertical-pattern-corrected target digital image to produce a dark-current-corrected target digital image.

9. The method of claim 8 also comprising applying an offset estimation and subtraction function to the dark-current-corrected target digital image to remove offset.

10. An apparatus comprising:

a software processor configured to

receive, from an image sensor, a target digital image T contaminated by noise of unknown magnitude A that is represented by a reference digital image; and

apply a process that uses a variance minimization analysis with respect to the unknown magnitude A associated with the target digital image T and the reference digital image, to determine the magnitude of the noise for at least some pixels of the target digital image,

wherein the determination using the variance minimization analysis is mathematically and statistically equivalent to a determination of the magnitude of the noise made from a decorrelation condition;

Correlation(T−

A*{the reference digital image},{the reference digital image})=0.

11. The apparatus of claim 10 further comprising the image sensor.

12. The apparatus of claim 11 in which the image sensor comprises a CMOS sensor.

13. The apparatus of claim 11 in which the image sensor comprises a CCD sensor.

14. The apparatus of claim 10 in which the noise comprises dark current noise.

15. The apparatus of claim 10 in which the target digital image comprises an image of one or more lesions on a patient.

16. The apparatus of claim 10 in which the determination is made over pixels in some regions of the target digital image T and the reference digital image, to estimate the magnitude of the noise for each of at least some pixels of target digital image T.

17. The apparatus of claim 10 in which the software processor is also configured to produce a noise-reduced version of the target digital image using the determined noise magnitude.

18. The apparatus of claim 17 in which the software processor is also configured to provide the noise-reduced version of the target digital image to a processor for use in analyzing features of an image captured by the sensor.

19. The apparatus of claim 18 in which the image captured by the sensor includes one or more lesions.

20. The apparatus of claim 19 in which the software processor is also configured to report a malignancy state of the one or more lesions to a user of the apparatus.

21. An apparatus comprising:

means for receiving a target digital image T contaminated by noise of unknown magnitude A that is represented by a reference digital image; and

means for applying a process that uses a variance minimization analysis with respect to the unknown magnitude A associated with the target digital image and the reference digital image, to determine the magnitude of the noise for at least some pixels of the target digital image,

wherein the determination using the variance minimization analysis is mathematically and statistically equivalent to a determination of the magnitude of the noise made from a decorrelation condition;

Correlation(T−

A*{the reference digital image},{the reference digital image})=0.