Image processing for removing a noise component contained in an image

• US 8,369,640 B2
• Filed: 07/24/2009
• Issued: 02/05/2013
• Est. Priority Date: 02/02/2007
• Status: Active Grant

• Current Assignee: Nikon Corporation
• Original Assignee: Nikon Corporation
• Inventors: Ishiga, Kenichi
• Primary Examiner(s): Akhavannik, Hadi
• Application Number:
  US12/458,855
• Publication Number:
  US 20090290067A1
• Time in Patent Office: 1,292 Days
• Field of Search: 382/254, 382/261
• US Class Current: 382/254
• CPC Class Codes: G06T 2207/20064 Wavelet transform [DWT]
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• 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 7/13 Edge detection
• CPC Class Codes: H04N 1/409 Edge or detail enhancement; Noise or error suppression

Abstract

An image processing method for removing a noise component contained in an original image, includes: separating an original image into a temporary noise-free image and a temporary noise component; extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image; determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component; extracting an actual noise component by excluding the fine edge component from the temporary noise component; and removing noise from the original image based upon the extracted actual noise component.

Claims

1. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein a probability of the fine edge component being present in the temporary noise component is estimated to be higher when the level of the edge component is greater relative to a noise fluctuation index value determined uniquely in correspondence to the original image.

2. An image processing method according to claim 1, wherein:

a mixing ratio with which the fine edge component is present is statistically estimated based upon a Gaussian distribution probability assuming a ratio of the edge component and the noise fluctuation index value as an argument.

3. An image processing method according to claim 2, wherein:

the fine edge component is excluded through attenuation by multiplying the temporary noise component by the Gaussian distribution probability.

11. An image processing method according to claim 1, wherein:

a uniform index value is used as the noise fluctuation index value irrespective of a brightness level of the original image by converting the original image to an image in uniform noise space or an image in a uniform color•

uniform noise space.

4. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein the original image is separated into the temporary noise-free image having undergone adaptive smoothing and the temporary noise component representing a residue thereof by setting a noise fluctuation index value determined uniquely in correspondence to the original image and comparing a signal fluctuation width detected in an original image structure with the noise fluctuation index value.

5. An image processing method according to claim 4, wherein:

the temporary noise-free image having undergone adaptive smoothing is generated through weighted averaging of signal values at pixels near a target pixel, which fluctuate over a narrow range equal to or less than a range indicated by the noise fluctuation value, the weighted averaging being selectively executed according to a signal differential value; and

the temporary noise component is obtained by determining a difference between the temporary noise-free image and the original image.

6. An image processing method according to claim 4, wherein:

the temporary noise component is extracted through a signal conversion processing phase in which only a component fluctuating over a small range equal to or less than a range indicated by the noise fluctuation index value in a differential signal representing a difference between the original image and a uniformly smooth image, is allowed to pass through; and

the temporary noise-free image is generated by subtracting the temporary noise component from the original image.

7. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein an image referencing range over which the edge detection is executed is set in correspondence to an image referencing range set for generation of the temporary noise-free image.

8. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein the original image is a band-limited subband image in multiresolution representation.

9. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein the original image is an original image expressed in an actual space.

10. An image processing method for removing a noise component contained in an original image, comprising:

separating an original image into a temporary noise-free image and a temporary noise component;

extracting an edge component in the temporary noise-free image by executing edge detection on the temporary noise-free image;

determining a fine edge component in the original image contained in the temporary noise component based upon a level of the extracted edge component;

extracting an actual noise component by excluding the fine edge component from the temporary noise component; and

removing noise from the original image based upon the extracted actual noise component;

wherein processing on the original image is executed individually on a luminance plane and a chrominance plane.