METHOD OF AND APPARATUS FOR ELIMINATING IMAGE NOISE

• US 20080118179A1
• Filed: 09/28/2007
• Published: 05/22/2008
• Est. Priority Date: 11/21/2006
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Park, Ju-hee, JEONG, Young-hoon, Lee, Keum-seok, Hwang, Won-kyu
• Granted Patent: US 8,050,509 B2
• Time in Patent Office: Days
• US Class Current: 382/275
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20182 Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06V 10/30 Noise filtering

Abstract

A method and apparatus are provided for eliminating image noise to remove spatial-temporal noise and improve visibility. The method includes extracting a spatial-temporal noise level of neighbor pixels around a current pixel, filtering noise of the current pixel by applying a weight to spatial-temporal pixels around the current pixel based on the extracted spatial-temporal noise level, and applying a weight to the noise-filtered pixel and a boosted-up pixel based on an edge intensity and summing the weight-applied pixels. The spatial-temporal noise level is extracted based on spatial-temporal information of neighbor pixels around a current pixel in a current frame and spatial-temporal information of neighbor pixels around a current pixel in a previous frame.

Claims

1. A method of eliminating image noise, the method comprising:

extracting a spatial-temporal noise level of neighbor pixels around a current pixel;

filtering noise of the current pixel by applying a weight to spatial-temporal pixels around the current pixel based on the extracted spatial-temporal noise level; and

applying a weight to the noise-filtered current pixel and a boosted-up pixel based on an edge intensity, and summing up the weight-applied pixels.

2. The method of claim 1, wherein the spatial-temporal noise level is extracted based on spatial-temporal information of neighbor pixels around a current pixel in a current frame and spatial-temporal information of neighbor pixels around a current pixel in a previous frame.

3. The method of claim 1, wherein the spatial-temporal noise level is extracted based on at least one of a spatial edge intensity of an image, a temporal correlation between the neighbor pixels, and a change in spatial-temporal brightness of the neighbor pixels.

4. The method of claim 1, wherein the extracting the spatial-temporal noise level comprises:

extracting a spatial edge intensity of an image, the neighbor pixels, a temporal correlation between the neighbor pixels, and a spatial-temporal brightness of the neighbor pixels; and

transforming the spatial edge intensity, the temporal correlation, and the spatial-temporal brightness to parameters using function-based equations.

5. The method of claim 4, wherein the extracting the spatial edge intensity comprises:

performing high-pass filtering on an image signal of a current frame in order to extract an edge intensity;

transforming the high-pass filtered signal using a function; and

transforming the transformed signal to an edge intensity parameter using a function-based equation.

6. The method of claim 4, wherein the extracting the temporal correlation comprises:

extracting a difference between neighbor pixels around a current pixel in a current frame and neighbor pixels around a current pixel in a previous frame or a next frame;

transforming the difference using a function; and

transforming the transformed difference to a correlation parameter using a function-based equation.

7. The method of claim 4, wherein the extracting the spatial-temporal brightness comprises:

extracting a pixel change around a current pixel in a current frame and a current pixel in a previous frame;

transforming the pixel change using a function; and

transforming the transformed pixel change to a brightness parameter using a function-based equation.

8. The method of claim 4, wherein the filtering of the noise comprises:

summing the spatial edge intensity parameter, the temporal correlation parameter, and the spatial-temporal brightness parameter;

calculating weights for the current pixel and its neighbor pixels based on a result of the summing and a difference between the current pixel and its neighbor pixels; and

calculating a weight average for the neighbor pixels around the current pixels in the current frame and the previous frame based on the calculated weights and replacing the weight average with the current pixel.

9. The method of claim 1, wherein the applying the weight based on the edge intensity comprises:

boosting up a pixel of an image signal of the current frame; and

applying the weight to the noise-filtered pixel and the boosted-up pixel based on the edge intensity, and summing the weight-applied pixels.

10. The method of claim 9, wherein the weight based on the edge intensity is a parameter determined by an output of a function that receives the edge intensity.

11. An image processing apparatus comprising:

a noise level extraction unit which extracts a spatial-temporal noise level of neighbor pixels around a pixel based on spatial-temporal information of neighbor pixels around a pixel in a current frame and spatial-temporal information of neighbor pixels around a current pixel in a previous frame;

a spatial-temporal noise elimination unit which filters noise of the current pixel by applying a weight to spatial-temporal pixels around the current pixel based on the spatial-temporal noise level extracted by the noise level extraction unit; and

an edge improvement unit which applies a weight to the noise-filtered current pixel obtained by the spatial-temporal noise elimination unit and a boosted-up pixel based on an edge intensity and sums the weight-applied pixels.

12. The image processing apparatus of claim 11, wherein the noise level extraction unit comprises:

an edge intensity parameter extraction unit which generates an edge intensity parameter by extracting the edge intensity by performing high-pass filtering on an image signal of the current frame;

a correlation parameter extraction unit which generates a correlation parameter by extracting a difference between neighbor pixels around the current pixel in the current frame and neighbor pixels around the current pixel in the previous frame or a next frame; and

a brightness parameter extraction unit which generates a brightness parameter by extracting a pixel change around the current pixels in the current frame and in the previous frame.

13. The image processing apparatus of claim 12, wherein the spatial-temporal noise elimination unit comprises:

an addition unit which sums the edge intensity parameter, the correlation parameter, and the brightness parameter;

a weight extraction unit which extracts weights for the current pixel and its neighbor pixels based on a summing result of the addition unit and a difference between the current pixel and its neighbor pixels; and

a noise filtering unit which calculates a weight average for the neighbor pixels around the current pixels in the current frame and the previous frame based on the calculated weights and replaces the weight average with the current pixel.

14. The image processing apparatus of claim 11, wherein the edge improvement unit comprises:

a boosting-up unit which boosts up a pixel of an image signal of the current frame; and

an edge visual-improvement unit which applies the weight to the noise-filtered pixel and the boosted-up pixel based on the edge intensity and sums the weight-applied pixels.