我想将butterworth filter应用于照片。过滤器设计方法是bilinear。
我设计的过滤器是:
[N, Wn]=buttord(2, 4.83, -3, -15, 's');
[b,a]=butter(N,Wn,'s');
[num,den]=bilinear(b,a,1);
如何将滤镜应用于2D图像?
答案 0 :(得分:2)
buttord
用于一维信号处理。您可以编写一个简单的代码来自行设计2D butterworth滤波器。下面是带通butterworth滤波器的示例代码。您可以删除高通滤镜上的d1
,或删除低通滤镜上的d0
。
filter1 = ones(2*nx-1,2*ny-1);
filter2 = ones(2*nx-1,2*ny-1);
filter3 = ones(2*nx-1,2*ny-1);
for i = 1:2*nx-1
for j =1:2*ny-1
d = ((i-(nx+1))^2 + (j-(ny+1))^2)^.5;
filter1(i,j)= 1/(1 + (d/d1)^(2*n)); % d1:higher cutoff frequency
filter2(i,j) = 1/(1 + (d/d0)^(2*n)); % d0:lower cutoff frequency
filter3(i,j)= 1 - filter2(i,j);
butterworthf(i,j) = filter1(i,j).*filter3(i,j); % Create Butterworth filter.
end
end
答案 1 :(得分:2)
您可以使用该链接上给出的代码:
代码为
function filtered_image = butterworthbpf(I,d0,d1,n)
% Butterworth Bandpass Filter
% This simple function was written for my Digital Image Processing course
% at Eastern Mediterranean University taught by
% Assoc. Prof. Dr. Hasan Demirel
% for the 2010-2011 Spring Semester
% for the complete report:
% http://www.scribd.com/doc/51981950/HW4-Frequency-Domain-Bandpass-Filtering
%
% Written By:
% Leonardo O. Iheme (leonardo.iheme@cc.emu.edu.tr)
% 23rd of March 2011
%
% I = The input grey scale image
% d0 = Lower cut off frequency
% d1 = Higher cut off frequency
% n = order of the filter
%
% The function makes use of the simple principle that a bandpass filter
% can be obtained by multiplying a lowpass filter with a highpass filter
% where the lowpass filter has a higher cut off frquency than the high pass filter.
%
% Usage BUTTERWORTHBPF(I,DO,D1,N)
% Example
% ima = imread('grass.jpg');
% ima = rgb2gray(ima);
% filtered_image = butterworthbpf(ima,30,120,4);
f = double(I);
[nx, ny] = size(f);
f = uint8(f);
fftI = fft2(f,2*nx-1,2*ny-1);
fftI = fftshift(fftI);
subplot(2,2,1)
imshow(f,[]);
title('Original Image')
subplot(2,2,2)
fftshow(fftI,'log')
title('Image in Fourier Domain')
% Initialize filter.
filter1 = ones(2*nx-1,2*ny-1);
filter2 = ones(2*nx-1,2*ny-1);
filter3 = ones(2*nx-1,2*ny-1);
for i = 1:2*nx-1
for j =1:2*ny-1
dist = ((i-(nx+1))^2 + (j-(ny+1))^2)^.5;
% Create Butterworth filter.
filter1(i,j)= 1/(1 + (dist/d1)^(2*n));
filter2(i,j) = 1/(1 + (dist/d0)^(2*n));
filter3(i,j)= 1.0 - filter2(i,j);
filter3(i,j) = filter1(i,j).*filter3(i,j);
end
end
% Update image with passed frequencies.
filtered_image = fftI + filter3.*fftI;
subplot(2,2,3)
fftshow(filter3,'log')
title('Filter Image')
filtered_image = ifftshift(filtered_image);
filtered_image = ifft2(filtered_image,2*nx-1,2*ny-1);
filtered_image = real(filtered_image(1:nx,1:ny));
filtered_image = uint8(filtered_image);
subplot(2,2,4)
imshow(filtered_image,[])
title('Filtered Image')
end
答案 2 :(得分:0)
我不知道MATLAB内置函数,但我写了一个可以用于Image的butterworth过滤器
function Filter = MyButterWorth(grade, cutoff_freq, img_name,type)
img=imread(img_name); %read image
[rows,cols] = size(img);
img_double=double(img);
FImg = fftshift(fft2(img_double)); %Fast Fourier transform 2D and shift it to Center
%immagphase(FImg);
% compute distance to center with consider image size
x = (ones(rows,1) * [1:cols] - (fix(cols/2)+1))/cols;
y = ([1:rows]' * ones(1,cols) - (fix(rows/2)+1))/rows;
radius = sqrt(x.^2 + y.^2);
% create filter
Filter = 1 ./ (1.0 + (radius ./ cutoff_freq).^(2*grade));
% change filter type low pass or high
if(strcmp(type, 'hpf'))
Filter = 1 - Filter;
end
figure;
surf([-1:2/(cols-1):1],[-1:2/(rows-1):1], Filter);
shading interp;
title('Butterworth filter');
xlabel('x');
ylabel('y');
zlabel('intensity');
grid on;
%applay filter
resultFImg = FImg .* Filter;
resultImg = real(ifft2(ifftshift(resultFImg)));
%show image
figure;
subplot(1,2,1); imshow(img); title('Orginal Image')
subplot(1,2,2); imshow(resultImg,[]); title('Filtered Image')
答案 3 :(得分:-2)
看看以下内容: