Methods and Systems for Image Noise Filtering

• US 20110182510A1
• Filed: 01/28/2010
• Published: 07/28/2011
• Est. Priority Date: 01/28/2010
• Status: Active Grant

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Hong, Wei
• Granted Patent: US 8,457,433 B2
• Time in Patent Office: Days
• US Class Current: 382/167
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 3/4015 Image demosaicing, e.g. colour filter arrays [CFA] or Bayer patterns
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

Methods and systems for image noise filtering are provided. One method of image noise filtering includes generating a plurality of sub-images of a digital image, applying a noise filter with small support to each sub-image, and generating a filtered digital image by merging the filtered sub-images. Another method of image noise filtering includes receiving a digital image including a plurality of color channels in the Bayer domain, applying a strong noise filter to each color channel to generate filtered color channels, computing a luminance image from the digital image, applying a weak noise filter to the luminance image to generate a filtered luminance image, computing a luminance recovery factor map using the luminance image and the filtered luminance image, and computing output color channels of the digital image using the filtered color channels and the luminance recovery factor map.

Claims

1. A method of image noise filtering comprising:

generating a plurality of sub-images of a digital image;

applying a noise filter with small support to each sub-image; and

generating a filtered digital image by merging the filtered sub-images.

2. The method of claim 1, wherein generating a plurality of sub-images comprises generating each sub-image I_ij(x′

,y′

) as I_ij(x′

,y′

)=1(x′

*m+i,y′

*n+j) for x′

=0 . . . W/m−

1, y′

=0 . . . H/n−

1 wherein W is a width in pixels in the digital image I(x, y), H is a height in pixels of the digital image I(x, y), m is a horizontal sub-sampling factor of each sub-image, n is a vertical sub-sampling factor of each sub-image, i=0 . . . m−

1, and j=0 . . . n−

1.

3. The method of claim 2, wherein generating a filtered digital image comprises merging each filtered sub-image If_ij(x′

,y′

) into the filtered digital image If(x, y) as If(x′

*m+i,y′

*n+j)=If_ij(x′

,y′

).

4. The method of claim 1, wherein the noise filter with small support is a noise filter of size s1×

s2 where s1 is less than or equal to 31 and s2 is less than or equal to 31.

5. The method of claim 1, wherein applying a noise filter comprises:

applying a strong noise filter to each color channel in a sub-image to generate filtered color channels, wherein chrominance noise in each color channel is suppressed in the corresponding filtered color channel;

computing a luminance image from the sub-image;

applying a weak noise filter to the luminance image to generate a filtered luminance image;

computing a luminance recovery factor map using the luminance image and the filtered luminance image; and

computing output color channels of the sub-image using the filtered color channels and the luminance recovery factor map.

6. The method of claim 5, wherein computing a luminance image comprises computing each pixel of the luminance image as an average of corresponding pixels in the color channels of the digital image.

7. The method of claim 6, wherein computing a luminance recovery factor map comprises computing each entry x in the luminance recovery factor map as Y(x,y)/(Y_f(x,y)+b) where b a number selected to prevent division by zero, Y(x,y) is a corresponding pixel in the luminance image and Y_f(x,y) is a corresponding pixel in the filtered luminance image.

8. The method of claim 7, wherein computing output color channels comprises for each filtered color channel, multiplying each pixel in the filtered color channel by a corresponding entry in the luminance recovery factor map.

9. A method of image noise filtering comprising:

receiving a digital image comprising a plurality of color channels in the Bayer domain;

applying a strong noise filter to each color channel to generate filtered color channels, wherein chrominance noise in each color channel is suppressed in the corresponding filtered color channel;

computing a luminance image from the digital image;

applying a weak noise filter to the luminance image to generate a filtered luminance image;

computing a luminance recovery factor map using the luminance image and the filtered luminance image; and

computing output color channels of the digital image using the filtered color channels and the luminance recovery factor map.

10. The method of claim 9, wherein computing a luminance image comprises computing each pixel of the luminance image as an average of corresponding pixels in the color channels of the digital image.

11. The method of claim 10, wherein computing a luminance recovery factor map comprises computing each entry x in the luminance recovery factor map as Y(x,y)/(Y_f(x,y)+b) where b a number selected to prevent division by zero, Y(x,y) is a corresponding pixel in the luminance image and Y_f(x,y) is a corresponding pixel in the filtered luminance image.

12. The method of claim 11, wherein computing output color channels comprises for each filtered color channel, multiplying each pixel in the filtered color channel by a corresponding entry in the luminance recovery factor map.

13. The method of claim 9, further comprising:

generating a plurality of sub-images of the digital image;

applying a noise filter with small support to each sub-image; and

generating a filtered digital image by merging the filtered sub-images.

14. The method of claim 9, wherein generating a plurality of sub-images comprises generating each sub-image I_ij(x′

,y′

) as I_ij(x′

,y′

)=I(x′

*m+i,y′

*n+j) for x′

=0 . . . W/m−

1, y′

=0 . . . H/n−

1 wherein W is a width in pixels in the digital image I(x, y), H is a height in pixels of the digital image I(x, y), m is a horizontal sub-sampling factor of each sub-image, n is a vertical sub-sampling factor of each sub-image, i=0 . . . m−

1, and j=0 . . . n−

1.

15. The method of claim 14, wherein generating a filtered digital image comprises merging each filtered sub-image If_ij(x′

,y′

) into the filtered digital image If(x, y) as If(x′

*m+i,y′

*n+j)=If_ij(x′

,y′

).

16. The method of claim 13, wherein the noise filter with small support is a noise filter of size s1×

s2 where s1 is less than or equal to 31 and s2 is less than or equal to 31.

17. A digital system comprising:

a processor; and

a memory storing software instructions, wherein when executed by the digital signal processor, the software instructions cause the digital system to perform a method comprising;

generating a plurality of sub-images of a digital image;

applying a noise filter with small support to each sub-image; and

generating a filtered digital image by merging the filtered sub-images.

18. The digital system of claim 17, wherein generating a plurality of sub-images comprises generating each sub-image I_ij(x′

,y′

) as I_ij(x′

,y′

)=1(x′

*m+i,y′

*n+j) for x′

=0 . . . W/m−

1, y′

=0 . . . H/n−

1 wherein W is a width in pixels in the digital image I(x, y), H is a height in pixels of the digital image I(x, y), m is a horizontal sub-sampling factor of each sub-image, n is a vertical sub-sampling factor of each sub-image, i=0 . . . m−

1, and j=0 . . . n−

1.

19. The digital system of claim 18, wherein generating a filtered digital image comprises merging each filtered sub-image If_ij(x′

,y′

) into the filtered digital image If(x, y) as If(x′

*m+i,y′

*n+j)=If_ij(x′

,y′

).

20. The digital system of claim 17, wherein the noise filter with small support is a noise filter of size s1×

s2 where s1 is less than or equal to 31 and s2 is less than or equal to 31.