Spatio-temporal joint filter for noise reduction

• US 20020028025A1
• Filed: 07/18/2001
• Published: 03/07/2002
• Est. Priority Date: 07/18/2000
• Status: Active Grant

• Current Assignee: LG Electronics, Inc. (LG Corporation)
• Original Assignee: LG Electronics, Inc. (LG Corporation)
• Inventors: Hong, Sung Hoon
• Granted Patent: US 6,999,634 B2
• Time in Patent Office: Days
• US Class Current: 382/260
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: Y10S 348/909 Noise responsive signal processing
• CPC Class Codes: Y10S 348/91 Flicker reduction

Abstract

A spatio-temporal joint filter and a spatial joint filter for noise reduction are disclosed. The spatio-temporal joint filter includes a spatial joint filter including the first and second sub filters having different characteristics and includes a temporal joint filter. When the present invention is adequately used, an edge/detail region of an image is well preserved, an aggressive noise reduction is performed on a flat region, and the temporal flicker problems are eliminated. Additionally, it has an intrinsic motion compensation effect by using the spatio-temporal correlation between the adjacent frames.

Claims

1. A spatial joint filter for noise reduction comprising:

a first filter determining a first weighted average of support region pixel signals corresponding to an input signal using first weighting factors, each of said first weighting factors depending on said input signal and each of said support region pixel signals; and

a second filter determining a second weighted sum of said input signal and said first weighted average using a second weighting factor that depends on an original signal variance and a noise variance.

2. The spatial joint filter of claim 1, wherein said first weighted average is close to an average of said support region pixel signals for a flat region of an image and is close to said input signal for a detail or edge region of said image.

3. The spatial joint filter of claim 1, wherein said first weighted average g1(i,j,t) is determined using the following equation:

4. The spatial joint filter of claim 3, wherein said first filter is AWA filter, and each first weighting factor {overscore (ω

)}(l,m,t) is determined using the following equation;

5. The spatial joint filter of claim 3, wherein said first filter is A-MEAN filter, and each first weighting factor is determined using the following question:

6. The spatial joint filter of claim 1, wherein said second weighted sum f_s(i,j,t) is determined using the following equation:

f_s(i,j,t)=α

(i,j,t)g(i,j,t)+[1−

α

(i,j,t)]g1(i,j,t)

and

7. The spatial joint filter of claim 6, wherein said original signal variance σ

_f²(i,j,t) is determined using the following equation;

8. The spatial joint filter of claim 7, wherein said local variance σ

_g²(i,j,t) is determined using the following equation;

9. A spatio-temporal joint filter for noise reduction comprising:

a spatial joint filter including

a first filter determining a first weighted average of current support region signals of an input signal using first weighting factors, said current support region signals being included in a current frame, and

a second filter determining a second weighted sum of said input signal and said first weighted average using a second weighting factor that depends on an original signal variance and a noise variance;

a temporal joint filter including

a third filter determining a third weighted average of said second weighted sum and previous support region signals of said input signal using third weighting factors, said previous support region signals being included in a previous frame, and

a forth filter determining a forth weighted sum of said second weighted sum and said third weighted average using a forth weighting factor that depends on a spatio-temporal signal variance and said noise variance; and

a frame memory storing said forth weighted sum and feedbacking said forth weighted sum to said third filter for a next frame.

10. The spatio-temporal joint filter of claim 9, wherein said first weighted average g1(i,j,t) is determined using the following equation:

11. The spatio-temporal joint filter of claim 10, wherein said first filter is AWA filter, and each first weighting factor {overscore (ω

)}(l,m,t) is determined using the following equation;

12. The spatio-temporal joint filter of claim 10, wherein said first filter is A-MEAN filter, and each said first weighting factor {overscore (ω

)}(l,m,t) is determined using the following question;

13. The spatio-temporal joint filter of claim 9, wherein said second weighted sum f_s(i,j,t) is determined using the following equation:

f_s(i,j,t)=α

(i,j,t)g(i,j,t)+[1−

α

(i,j,t)]g1(i,j,t)

and

14. The spatio-temporal joint filter of claim 13, wherein said original signal variance σ

_f²(i,j,t) is determined using the following equation;

σ

_f²(i,j,t)=max[σ

_g²(i,j,t)−

σ

_n²,0]

where σ

_g²(i,j,t) is a local variance of said input signal g(i,j,t).

15. The spatio-temporal joint filter of claim 14, wherein said local variance σ

_g²(i,j,t) is determined using the following equation;

16. The spatio-temporal joint filter of claim 9, wherein said third weighted average μ

_T(i,j,t) is determined using the following equation;

17. The spatio-temporal joint filter of claim 16, wherein said third filter is an AWA filter, and

18. The spatio-temporal joint filter of claim 16, wherein said third filter is an A-MEAN filter, and

19. The spatio-temporal joint filter of claim 9, wherein said forth weighted sum f_o(i,j,t) is determined using the following equation:

20. The spatio-temporal joint filter of claim 19,