clear all
clc
I1=imread('1234.jpg')
subplot(2,2,1)
imshow(I1)
title('原始图像')
I2=imnoise(I1,'salt & pepper')
subplot(2,2,2)
imshow(I2)
title('噪声图像')
f=double(I2)
g=fft2(f)
g=fftshift(g)
[N1,N2]=size(g)
n=2
d0=50
n1=fix(N1/2)
n2=fix(N2/2)
for i = 1:N1
for j=2:N2
d=sqrt((i-n1)^2+(j-n2)^2);
h=1/(1+0.414*(d/d0)^(2*n));
result1(i,j)=h * g(i,j);
if(g(i,j)<50)
result2(i,j) = 0
else
result2(i,j)=g(i,j)
L = labimg(:,:,1);
A = labimg(:,:,2);
B = labimg(:,:,3);
[M, N] = size(L);
s = M*N;
temp = L;
temp(1, :) = [];
L_rise = [temp; L(M, :)];
temp = L;
temp(M, :) = [];
L_fall = [L(1, :); temp];
temp = L;
temp(:, N) = [];
L_right = [L(:, 1) temp];
temp = L;
temp(:, 1) = [];
L_left = [temp L(:, N)];
L_edge = abs(L_fall-L_rise)+abs(L_right-L_left);
temp = A;
temp(1, :) = [];
A_rise = [temp; A(M, :)];
temp = A;
temp(M, :) = [];
A_fall = [A(1, :); temp];
temp = A;
temp(:, N) = [];
A_right = [A(:, 1) temp];
temp = A;
temp(:, 1) = [];
A_left = [temp A(:, N)];
A_edge = abs(A_fall-A_rise)+abs(A_right-A_left);
temp = B;
temp(1, :) = [];
B_rise = [temp; B(M, :)];
temp = B;
temp(M, :) = [];
B_fall = [B(1, :); temp];
temp = B;
temp(:, N) = [];
B_right = [B(:, 1) temp];
temp = B;
temp(:, 1) = [];
B_left = [temp B(:, N)];
B_edge = abs(B_fall-B_rise)+abs(B_right-B_left);
labedge = L_edge + A_edge + B_edge
function klabels = PerformSuperpixelSLIC(img_Lab, kseedsl, kseedsa, kseedsb, kseedsx, kseedsy, STEP, compactness)
[m_height, m_width, m_channel] = size(img_Lab);
numseeds = size(kseedsl);
img_Lab = double(img_Lab);
%像素标签格式为(x, y) (行, 列)
klabels = zeros(m_height, m_width);
%聚类尺寸
clustersize = zeros(numseeds, 1);
inv = zeros(numseeds, 1);
sigmal = zeros(numseeds, 1);
sigmaa = zeros(numseeds, 1);
sigmab = zeros(numseeds, 1);
sigmax = zeros(numseeds, 1);
sigmay = zeros(numseeds, 1);
invwt = 1/((double(STEP)/double(compactness))*(double(STEP)/double(compactness)));
%invwt = double(compactness)/double(STEP);
distvec = 100000*ones(m_height, m_width);
numk = numseeds;
for itr = 1: 10 %迭代次数
sigmal = zeros(numseeds, 1);
sigmaa = zeros(numseeds, 1);
sigmab = zeros(numseeds, 1);
sigmax = zeros(numseeds, 1);
sigmay = zeros(numseeds, 1);
clustersize = zeros(numseeds, 1);
inv = zeros(numseeds, 1);
distvec = double(100000*ones(m_height, m_width));
%根据当前种子点信息计算每一个像素的归属
for n = 1: numk
y1 = max(1, kseedsy(n, 1)-STEP);
y2 = min(m_height, kseedsy(n, 1)+STEP);
x1 = max(1, kseedsx(n, 1)-STEP);
x2 = min(m_width, kseedsx(n, 1)+STEP);
%按像素计算距离
for y = y1: y2
for x = x1: x2
%dist_lab = abs(img_Lab(y, x, 1)-kseedsl(n))+abs(img_Lab(y, x, 2)-kseedsa(n))+abs(img_Lab(y, x, 3)-kseedsb(n));
dist_lab = (img_Lab(y, x, 1)-kseedsl(n, 1))^2+(img_Lab(y, x, 2)-kseedsa(n, 1))^2+(img_Lab(y, x, 3)-kseedsb(n, 1))^2;
dist_xy = (double(y)-kseedsy(n, 1))*(double(y)-kseedsy(n, 1)) + (double(x)-kseedsx(n, 1))*(double(x)-kseedsx(n, 1));
%dist_xy = abs(y-kseedsy(n)) + abs(x-kseedsx(n));
%距离 = lab色彩空间距离 + 空间距离权重×空间距离
dist = dist_lab + dist_xy*invwt;
%在周围最多四个种子点中找到最相似的 标记后存入klabels
%m = (y-1)*m_width+x;
if (dist<distvec(y, x))
distvec(y, x) = dist;
klabels(y, x) = n;
end
end
end
end
%重新计算种子点位置 使其向梯度最小地方移动
ind = 1;
for r = 1: m_height
for c = 1: m_width
sigmal(klabels(r, c),1) = sigmal(klabels(r, c),1)+img_Lab(r, c, 1);
sigmaa(klabels(r, c),1) = sigmaa(klabels(r, c),1)+img_Lab(r, c, 2);
sigmab(klabels(r, c),1) = sigmab(klabels(r, c),1)+img_Lab(r, c, 3);
sigmax(klabels(r, c),1) = sigmax(klabels(r, c),1)+c;
sigmay(klabels(r, c),1) = sigmay(klabels(r, c),1)+r;
clustersize(klabels(r, c),1) = clustersize(klabels(r, c),1)+1;
end
end
for m = 1: numseeds
if (clustersize(m, 1)<=0)
clustersize(m, 1) = 1;
end
inv(m, 1) = 1/clustersize(m, 1);
end
for m = 1: numseeds
kseedsl(m, 1) = sigmal(m, 1)*inv(m, 1);
kseedsa(m, 1) = sigmaa(m, 1)*inv(m, 1);
kseedsb(m, 1) = sigmab(m, 1)*inv(m, 1);
kseedsx(m, 1) = sigmax(m, 1)*inv(m, 1);
kseedsy(m, 1) = sigmay(m, 1)*inv(m, 1);
end
end
end
end
result1=ifftshift(result1);
result2=ifftshift(result2);
X2=ifft(result1);
x3=uint8(real(X2));
subplot(2,2,3)
title('巴特沃斯低通滤波图像')
X4=ifft2(result2);
X5=plot(2,2,4);
imshow(X5);
title('理想低通滤波图像')
