快速双边滤波源码「建议收藏」

全栈程序员-站长 • 2022年4月16日下午11:00 • 未分类 • 阅读 60

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快速双边滤波源码「建议收藏」

% Fast Bilateral Filter Using Raised Cosines

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% inImg : grayscale image
% sigma1 : width of spatial Gaussian
% sigma2 : width of range Gaussian
% [-w, w]^2 : domain of spatial Gaussian
% tol : truncation error
%
% Author: Kunal N. Chaudhury.
% Date: March 1, 2012.
% Modified: June 21, 2014.
%
% References:
% [1] K.N. Chaudhury, D. Sage, and M. Unser, “Fast O(1) bilateral
% filtering using trigonometric range kernels,” IEEE Trans. Image Proc.,
% vol. 20, no. 11, 2011.
%
% [2] K.N. Chaudhury, “Acceleration of the shiftable O(1) algorithm for
% bilateral filtering and non-local means,” IEEE Transactions on Image Proc.,
% vol. 22, no. 4, 2013.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% load test image
clc, clear all, close all force;
Img = double( imread(‘./images/ckb.jpg’) );
[m, n] = size(Img);

% create noisy image (additive Gaussian noise)
sigma = 20;
inImg = Img + sigma * randn(m, n);

% filter parameters
sigma1 = 4;
sigma2 = 30;
tol = 0.01;

% Set window for spatial Gaussian
w = 6*sigma1;
if (mod(w,2) == 0)
w = w+1;
end

% call bilateral filter
tic;
[outImg, param] = shiftableBF(inImg, sigma1, sigma2, w, tol);
toc;

% plot results
T = param.T;
N = param.N;
M = param.M;
gamma = 1 / (sqrt(N) * sigma2);
twoN = 2^N;

warning(‘off’); %#ok<WNOFF>

s = linspace(-T, T, 200);
g = exp( -s.^2 / (2 * sigma2 *sigma2) );
gApprox = cos(gamma * s).^N;
if M == 0
gTrunc = gApprox;
else
gTrunc = zeros( 1, length(s) );
for k = M : N – M
gTrunc = gTrunc + (nchoosek(N, k) / twoN) * …
cos( (2*k – N) * gamma * s );
end
end

figure(‘Units’,’normalized’,’Position’,[0 0.5 1 0.5]);
plot(s, g, ‘b’);
hold on,
plot(s, gApprox, ‘m’),
hold on,
plot(s, gTrunc, ‘r’);
axis(‘tight’), grid(‘on’),
legend(‘Gassian’,’Raised cosine’,’Truncated raised cosine’,’FontSize’, 10);
title(‘Comparison of the range kernels’, ‘FontSize’, 10),

peak = 255;
PSNR0 = 10 * log10(m * n * peak^2 / sum(sum( (inImg – Img).^2)) );
PSNR1 = 10 * log10(m * n * peak^2 / sum(sum((outImg – Img).^2)) );

figure(‘Units’,’normalized’,’Position’,[0 0.5 1 0.5]);
colormap gray,
subplot(1,3,1), imshow(uint8(Img)),
title(‘Original’, ‘FontSize’, 10), axis(‘image’, ‘off’);
subplot(1,3,2), imshow(uint8(inImg)),
title([ ‘Noisy, ‘, num2str(PSNR0, ‘%.2f’), ‘dB’] , ‘FontSize’, 10),
axis(‘image’, ‘off’);
subplot(1,3,3), imshow(uint8(outImg)),
title([ ‘Filtered, ‘, num2str(PSNR1, ‘%.2f’), ‘dB’] , ‘FontSize’, 10),
axis(‘image’, ‘off’);

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