Home > programming, research resources > Nice implementation of bilateral filtering

## Nice implementation of bilateral filtering

Nice implementation, e.g. the progress bar.

``` % BFILTER2 Two dimensional bilateral filtering. %    This function implements 2-D bilateral filtering using %    the method outlined in: % %       C. Tomasi and R. Manduchi. Bilateral Filtering for %       Gray and Color Images. In Proceedings of the IEEE %       International Conference on Computer Vision, 1998. % %    B = bfilter2(A,W,SIGMA) performs 2-D bilateral filtering %    for the grayscale or color image A. A should be a double %    precision matrix of size NxMx1 or NxMx3 (i.e., grayscale %    or color images, respectively) with normalized values in %    the closed interval [0,1]. The half-size of the Gaussian %    bilateral filter window is defined by W. The standard %    deviations of the bilateral filter are given by SIGMA, %    where the spatial-domain standard deviation is given by %    SIGMA(1) and the intensity-domain standard deviation is %    given by SIGMA(2). % % Douglas R. Lanman, Brown University, September 2006. % dlanman@brown.edu, http://mesh.brown.edu/dlanman```

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Pre-process input and select appropriate filter.
function B = bfilter2(A,w,sigma)

% Verify that the input image exists and is valid.
if ~exist(‘A’,’var’) || isempty(A)
error(‘Input image A is undefined or invalid.’);
end
if ~isfloat(A) || ~sum([1,3] == size(A,3)) || …
min(A(:)) < 0 || max(A(:)) > 1
error([‘Input image A must be a double precision ‘,…
‘matrix of size NxMx1 or NxMx3 on the closed ‘,…
‘interval [0,1].’]);
end

% Verify bilateral filter window size.
if ~exist(‘w’,’var’) || isempty(w) || …
numel(w) ~= 1 || w < 1
w = 5;
end
w = ceil(w);

% Verify bilateral filter standard deviations.
if ~exist(‘sigma’,’var’) || isempty(sigma) || …
numel(sigma) ~= 2 || sigma(1) <= 0 || sigma(2) <= 0
sigma = [3 0.1];
end

% Apply either grayscale or color bilateral filtering.
if size(A,3) == 1
B = bfltGray(A,w,sigma(1),sigma(2));
else
B = bfltColor(A,w,sigma(1),sigma(2));
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implements bilateral filtering for grayscale images.
function B = bfltGray(A,w,sigma_d,sigma_r)

% Pre-compute Gaussian distance weights.
[X,Y] = meshgrid(-w:w,-w:w);
G = exp(-(X.^2+Y.^2)/(2*sigma_d^2));

% Create waitbar.
h = waitbar(0,’Applying bilateral filter…’);
set(h,’Name’,’Bilateral Filter Progress’);

% Apply bilateral filter.
dim = size(A);
B = zeros(dim);
for i = 1:dim(1)
for j = 1:dim(2)

% Extract local region.
iMin = max(i-w,1);
iMax = min(i+w,dim(1));
jMin = max(j-w,1);
jMax = min(j+w,dim(2));
I = A(iMin:iMax,jMin:jMax);

% Compute Gaussian intensity weights.
H = exp(-(I-A(i,j)).^2/(2*sigma_r^2));

% Calculate bilateral filter response.
F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1);
B(i,j) = sum(F(:).*I(:))/sum(F(:));

end
waitbar(i/dim(1));
end

% Close waitbar.
close(h);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Implements bilateral filter for color images.
function B = bfltColor(A,w,sigma_d,sigma_r)

% Convert input sRGB image to CIELab color space.
if exist(‘applycform’,’file’)
A = applycform(A,makecform(‘srgb2lab’));
else
A = colorspace(‘Lab<-RGB’,A);
end

% Pre-compute Gaussian domain weights.
[X,Y] = meshgrid(-w:w,-w:w);
G = exp(-(X.^2+Y.^2)/(2*sigma_d^2));

% Rescale range variance (using maximum luminance).
sigma_r = 100*sigma_r;

% Create waitbar.
h = waitbar(0,’Applying bilateral filter…’);
set(h,’Name’,’Bilateral Filter Progress’);

% Apply bilateral filter.
dim = size(A);
B = zeros(dim);
for i = 1:dim(1)
for j = 1:dim(2)

% Extract local region.
iMin = max(i-w,1);
iMax = min(i+w,dim(1));
jMin = max(j-w,1);
jMax = min(j+w,dim(2));
I = A(iMin:iMax,jMin:jMax,:);

% Compute Gaussian range weights.
dL = I(:,:,1)-A(i,j,1);
da = I(:,:,2)-A(i,j,2);
db = I(:,:,3)-A(i,j,3);
H = exp(-(dL.^2+da.^2+db.^2)/(2*sigma_r^2));

% Calculate bilateral filter response.
F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1);
norm_F = sum(F(:));
B(i,j,1) = sum(sum(F.*I(:,:,1)))/norm_F;
B(i,j,2) = sum(sum(F.*I(:,:,2)))/norm_F;
B(i,j,3) = sum(sum(F.*I(:,:,3)))/norm_F;

end
waitbar(i/dim(1));
end

% Convert filtered image back to sRGB color space.
if exist(‘applycform’,’file’)
B = applycform(B,makecform(‘lab2srgb’));
else
B = colorspace(‘RGB<-Lab’,B);
end

% Close waitbar.
close(h);