System and method for reduction of speckle noise in an image

• US 8,208,724 B2
• Filed: 05/27/2011
• Issued: 06/26/2012
• 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 B
• Application Number:
  US13/118,165
• Publication Number:
  US 20110229034A1
• Time in Patent Office: 396 Days
• Field of Search: 382128-134, 382/144, 382/173, 382/260, 382/261, 382/274, 382/275, 382/224, 600/437, 600/443, 358/463
• US Class Current: 382/173
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• 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 segmenting an image comprising:

(a) receiving an image comprising a plurality of pixels;

(b) establishing a coherence parameter and a number of classes;

(c) for each of the plurality of pixels, comparing an intensity of each pixel to an intensity of one or more neighboring pixels;

(d) classifying each pixel into a class in response to a maximum value of a conditional probability function in response to the intensity of each pixel; and

(e) providing a segmented image in response to the classification of each of the plurality of pixels.

2. The method of claim 1 wherein step (c) further comprises minimizing a cost function.

3. The method of claim 2 wherein the cost function comprises a convex Gibbs energy function.

4. The method of claim 2, wherein minimizing the cost function includes maximizing the conditional probability function for each pixel in the image.

5. The method of claim 1 wherein step (c) further comprises, for each pixel, computing the intensity of each pixel over all possible grey-level values.

6. The method of claim 1 wherein the conditional probability function is derived from one of a Gibbs distribution or a Markov random field distribution.

7. The method of claim 1 further comprising the step:

(f) prior to performing step (a), removing speckle noise from the image.

8. The method of claim 1, wherein providing the segmented image includes providing the segmented image in a coherent imaging system.

9. The method of claim 8, wherein providing the segmented image in the coherent imaging system includes providing the segmented image in a synthetic aperture radar system or an ultrasonic imaging system.

10. A computer program product comprising:

a non-transitory computer readable medium comprising;

code to cause at least an image processor to receive an image comprising a plurality of pixels;

code to cause at least an image processor to establish a coherence parameter and a number of classes;

code to cause at least an image processor to compare an intensity of each pixel to the intensity of one or more of a neighboring pixel, for each of the plurality of pixels;

code to cause at least an image processor to classify each pixel into a class in response to a maximum value of a conditional probability function in response to the intensity of each pixel; and

code to cause at least an image processor to provide a segmented image in response to the classification of each of the plurality of pixels.

11. The product of claim 10 further comprising code to cause at least an image processor to minimize a cost function in order to compare the intensity of each pixel to the intensity of one or more of a neighboring pixel.

12. The product of claim 11, wherein the code to cause at least an image processor to minimize a cost function includes code to maximize the conditional probability function for each pixel in the image.

13. The product of claim 11 wherein the cost function comprises a convex Gibbs energy function.

14. The product of claim 10 further comprising code for causing at least an image processor to compute, for each pixel, the intensity of each pixel over all possible grey-level values.

15. The product of claim 10 further comprising code to cause at least an image processor to remove speckle noise from the image prior to receiving the image.

16. The product of claim 10, wherein the code to cause at least the image processor to provide the segmented image includes code to provide the segmented image in a coherent imaging system.

17. The product of claim 16, wherein the code to provide the segmented image in the coherent imaging system includes code to provide the segmented image in a synthetic aperture radar system or an ultrasonic imaging system.