System and method for reduction of speckle noise in an image

• US 8,184,927 B2
• Filed: 03/18/2009
• Issued: 05/22/2012
• Est. Priority Date: 07/31/2006
• Status: Active Grant

• Current Assignee: UNM Rainforest Innovations (f/k/a STC.UNM) (The University of New Mexico)
• Original Assignee: UNM Rainforest Innovations (f/k/a STC.UNM) (The University of New Mexico)
• Inventors: Lankoande, Ousseini, Hayat, Majeed M., Santhanam, Balu
• Primary Examiner(s): Patel, Kanjibhai
• Application Number:
  US12/406,730
• Publication Number:
  US 20090175557A1
• Time in Patent Office: 1,161 Days
• Field of Search: 382128-134, 382/144, 382/173, 382260-261, 382274-275, 600/437, 600/443, 358/463
• US Class Current: 382/275
• CPC Class Codes: G01S 13/9027 Pattern recognition for feature extraction
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 20/13 Satellite images

Abstract

The present invention includes methods for the reduction of speckle noise in an image and methods for segmenting an image. Each of the methods disclosed herein includes steps for analyzing the uniformity of a pixel within a plurality of pixels forming a portion of the image and, based on the uniformity of the intensity of the plurality of pixels, adjusting and/or replacing the pixel in order to produce a speckle-noise reduced image, a segmented image, or a segmented and speckle-noise reduced image. The methods of the present invention can employ for example conditional probability density functions, nonlinear estimator functions, convex energy functions and simulated annealing algorithms in the performance of their respective steps.

Claims

1. A method of reducing speckle noise in an image using a processor and a memory, comprising:

(a) receiving an image comprising a plurality of pixels, the plurality of pixels defining a neighborhood system for pixels, wherein the neighborhood system for pixels comprises a first pixel and one or more neighboring pixels;

(b) estimating a coherence factor of the plurality of pixels;

(c) performing a uniformity test on a subset of pixels comprising a portion of the plurality of pixels;

(d) performing an intensity update on a pixel within the subset of pixels in response to the pixel being substantially non-uniform with respect to its neighborhood; and

(e) repeating steps (c) and (d) for substantially all of the plurality of pixels thereby producing a speckle-noise reduced image.

2. The method of claim 1, further comprising the step of performing a noise detection test on the subset of pixels.

3. The method of claim 1, further comprising the step of establishing a noise threshold factor and a pixel threshold factor.

4. The method of claim 1, wherein the step of estimating a coherence factor comprises solving a pseudo-likelihood estimate function.

5. The method of claim 4, wherein the step of solving a pseudo-likelihood estimate function comprises a fixed-point iteration solution method.

6. The method of claim 4, wherein the step of solving a pseudo-likelihood estimate function comprises a secant solution method.

7. The method of claim 4, wherein the step of solving a pseudo-likelihood estimate function comprises Brent'"'"'s method.

8. The method of claim 1 wherein the intensity update of step (d) comprises a non-linear estimator function.

9. The method of claim 8, wherein the non-linear estimator function comprises one of a convex Gibbs energy function or a Markov random field distribution function of at least a portion of the plurality of pixels.

10. The method of claim 8 wherein the non-linear estimator function receives as inputs an intensity value of at least one neighboring pixel.

11. A non-transitory storage media comprising program instructions that are hardware computer-executable to implement the reduction of speckle noise in an image, the storage media comprising:

program instructions that cause a processor to receive an image comprising a plurality of pixels, the plurality of pixels deigning a neighborhood system for pixels, wherein the neighborhood system for pixels comprises a first pixel and one or more neighboring pixels;

program instructions that cause a processor to estimate a coherence factor of the plurality of pixels;

program instructions that cause a processor to perform a uniformity test on a subset of pixels comprising a portion of the plurality of pixels;

program instructions that cause a processor to perform an intensity update on a pixel within the subset of pixels in response to the pixel being substantially non-uniform with respect to its neighborhood; and

program instructions that cause a processor to repeat the uniformity test and the intensity update for substantially all of the plurality of pixels thereby producing a speckle-noise reduced image.

12. The storage media of claim 11, further comprising program instructions that cause a processor to perform a noise detection test on the subset of pixels.

13. The storage media of claim 11, further comprising program instructions that cause a processor to establish a noise threshold factor and a pixel threshold factor.

14. The storage media of claim 11, wherein the program instructions that cause a processor to estimate a coherence factor comprises program instructions to cause a processor to solve a pseudo-likelihood estimate function.

15. The storage media of claim 14, wherein the program instructions to cause a processor to solve a pseudo-likelihood estimate function comprises program instructions that cause a processor to perform a fixed-point iteration solution method.

16. The storage media of claim 14, wherein the program instructions to cause a processor to solve a pseudo-likelihood estimate function comprises program instructions that cause a processor to perform a secant solution method.

17. The storage media of claim 14, wherein the program instructions to cause a processor to solve a pseudo-likelihood estimate function comprises program instructions that cause a processor to perform Brent'"'"'s method.

18. The storage media of claim 11 wherein the program instructions to cause a processor to perform an intensity update comprises program instructions that cause a processor to perform a non-linear estimator function.

19. The storage media of claim 18, wherein the non-linear estimator function comprises program instructions that cause a processor to perform one of a convex Gibbs energy function or a Markov random field distribution function of at least a portion of the plurality of pixels.

20. The storage media of claim 18, wherein the non-linear estimator function receives as inputs an intensity value of at least one neighboring pixel.

21. A system for reducing speckle noise in an image comprising:

a processor adapted to;

receive an image comprising a plurality of pixels, the plurality of pixels defining a neighborhood system for pixels, wherein the neighborhood system for pixels comprises a first pixel and one or more neighboring pixels;

estimate a coherence factor of the plurality of pixels;

perform a uniformity test on a subset of pixels comprising a portion of the plurality of pixels;

perform an intensity update on a pixel within the subset of pixels in response to the pixel being substantially non-uniform with respect to its neighborhood; and

generate a speckle reduced image.

22. The system of claim 21, further comprising an imaging device connected to the processor.

23. The system of claim 21, further comprising a display apparatus connected to the processor.