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### Highlights from Bilateral Filtering

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# Bilateral Filtering

06 Sep 2006

Implements bilateral filtering for grayscale and color images.

File Information
Description

Bilateral filtering was proposed by Tomasi and Manduchi in 1998 as a non-iterative method for edge-preserving smoothing. For a complete description see the following publication:

C. Tomasi and R. Manduchi. Bilateral Filtering for Gray and Color Images. In Proceedings of the IEEE International Conference on Computer Vision, 1998.

This package contains a basic implementation (bfilter2.m) of their method for both grayscale and color images. In order to demonstrate the utility of bilateral filtering, the main function is used to implement an automatic image abstraction routine (cartoon.m) inspired by:

Holger Winnemoller, Sven C. Olsen, and Bruce Gooch. Real-Time Video Abstraction. In Proceedings of ACM SIGGRAPH, 2006.

A demonstration program (runDemo.m) summarizes the typical use of all included files.

A gallery of image and video abstraction results is available at: http://mesh.brown.edu/dlanman/photos/Bilateral

Acknowledgements

This file inspired Retrogressive "Classic Game" Image Processing.

MATLAB release MATLAB 7.3 (R2006b)
27 Jul 2013
16 Jul 2013
16 Jul 2013

Simple and nice review on bilater filtering, using this file can be found here:
http://imageprocessingblog.com/bilateral-filtering/

28 Oct 2012

24 Aug 2012

thank you. works great!

06 Jun 2012
02 Nov 2011

First of all, thank you for the code but, while using with mat lab 6.1 , i am getting errors
as
??? Error: File: C:\bfilter2\Bilateral Filtering\bfilter2.m Line: 30 Column: 23
Expected a variable, function, or constant, found "|".

Error in ==> C:\bfilter2\Bilateral Filtering\runDemo.m
On line 30 ==> bflt_img1 = bfilter2(img1,w,sigma);

Tell me how to correct the error.

31 Oct 2011
04 Jan 2011

it is useful,thanks very much.

21 Jun 2010
12 Apr 2010

Hello, thank for codes [which works nicely both in gray-level and color image]

Mr Douglas Lanman or Mr. Joseph Shtok,
I have understood the bilateral filter over 2D dimension / an image.by considering Kernel which is a 2D square .

I have question. I'm apply bilateral filter over a sequence of images [video].
In one part of paper it applies 2D bilateral over a frame [which its code is here ]
but in other part of paper, it acquired 1-D bilateral filter for each pixel over frames. question is how?!!
I can't realize kernel here. but Mr. Joseph Shtok mentioned it's simple. Can you please explain?
Best Regards
Ali

23 Feb 2010

sankiiiiiuuuu :)

10 Feb 2010

Hi everybody and thanks for the code. I'm a student in an engineering school in France and I'm working on image denoising. Why applying bilateral filtering twice on an image is better than once ?

Regards,
Regis Melnotte

27 Jan 2010

Great codes and easy to read except for low speed. I try to extend it to 3-D case, however, it's very slow. Could you convert it to the matrix or vector computation instead of loop pixel by pixel?

14 Dec 2009

The code reduces easily and correctly to the one-dimensional case by slight modification of the bfilter2.m. Thank you!

08 Feb 2009

First of all, thank you for the code but, while using with mat lab 7.0 , i am getting errors
as

??? Error: File: Z:\Bilateral Filtering\bfilter2.m Line: 29 Column: 1
Function definitions are not permitted at the prompt or in scripts.

Error in ==> runDemo at 30
bflt_img1 = bfilter2(img1,w,sigma);

17 Dec 2008
06 Aug 2008

Frederic,

Thank you for your comments. I believe the code you referred to, specifically lines 67 and 114 in bfilter2.m, is correct as written. These lines implement the c(\xi,x) equation from Section 2.1 from the Tomasi and Manduchi paper. As they state, c is radially symmetric, with the d(\xi,x) term corresponding to the Euclidean distance between \xi and x. Expanding these expressions, the numerator of the exponent should be -(X.^2+Y.^2), not -(X-Y).^2. Your concern might come from how I've used the G matrix on these lines. This matrix is used to pre-compute c(\xi,x) for a w-by-w sized window. In this case, the origin is always the center pixel, so \sqrt((X.^2+Y.^2)) will represent the Euclidean distance of a pixel from the center of the window, where X and Y are pixel coordinates with respect to the center pixel, i.e. as given on lines 66 and 113. These geometric terms are identical for all windows, which is why I pre-computed their values. Unfortunately, the "intensity similarity" will vary for each w-by-w window. As a result, the H matrix, as given by lines 87 and 140, must be evaluated independently using a "sliding window" analysis.

Please send me an email if you have any further questions about my implementation.

Best regards,
Doug

06 Aug 2008

First of all, thank you for the code but, according to the Tomasi and Manduchi paper, I think that instead of:

G = exp(-(X.^2+Y.^2)/(2*sigma_d^2));

it should be:

G = exp(-(X-Y).^2/(2*sigma_d^2));

with respect to the GausSian distance weight in the bfilter2.m file.

Please, correct me if I'm wrong.

Bests,

16 Jul 2008

Thanks alot :)

02 Jul 2008

Thanks for the hard work. I appreciate the code.

29 Apr 2008

Thanks for the code

18 Jan 2008

good

18 Dec 2007
21 Nov 2007
27 Oct 2007

Excellent method except the speed. But a lot of papers are coming out. You can go to the SIGGRAPH07 for some information.

10 Oct 2007
30 Aug 2007
21 Mar 2007

Great filter but rather slow.
Do you know if it can be vectorised or sped up in some other way?
If there is anyone out there that knows how to speed up this code please let me know.
Good work!

04 Dec 2006

Very easy to understand and use! Comments precede each part. Run perfectly!

15 Nov 2006