function butterworth_low_Callback(hObject, eventdata, handles)%巴特沃斯低通滤波 % hObject handle to butterworth_low (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) axis off %%关闭坐标轴显示 global im; %%声明全局变量 global str; im=imread(str); I=im2double(im); A=imnoise(I,'salt & pepper',0.06); %椒盐噪声 J=fftshift(fft2(A)); [x, y]=meshgrid(-128:127, -128:127); %产生离散数据 z=sqrt(x.^2+y.^2); %幅度值 D1=40; %滤波器的截止频率 n=4; %滤波器的阶数 H1=1./(1+( z/D1).^(2*n)); %滤波器1 K1=J.*H1; %滤波 L1=ifft2(ifftshift(K1)); %傅里叶反变换 axes(handles.axes2); imshow(real(L1));给我修改一下
时间: 2024-03-08 11:45:39 浏览: 28
function butterworth_low_Callback(hObject, eventdata, handles)%巴特沃斯低通滤波
% hObject handle to butterworth_low (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
axis off; %%关闭坐标轴显示
global im; %%声明全局变量
global str;
im = imread(str);
I = im2double(im);
A = imnoise(I,'salt & pepper',0.06); %椒盐噪声
J = fftshift(fft2(A));
[x, y] = meshgrid(-128:127, -128:127); %产生离散数据
z = sqrt(x.^2 + y.^2); %幅度值
D1 = 40; %滤波器的截止频率
n = 4; %滤波器的阶数
H1 = 1./(1 + (z/D1).^(2*n)); %滤波器1
K1 = J.*H1; %滤波
L1 = real(ifft2(ifftshift(K1))); %傅里叶反变换并取实部
axes(handles.axes2);
imshow(L1); %显示滤波后的图像
相关问题
function butterworth_low_Callback(hObject, eventdata, handles)使用巴特沃斯低通滤波器对一幅加噪图像进行平滑处理的matlab代码
以下是使用巴特沃斯低通滤波器对一幅加噪图像进行平滑处理的MATLAB代码:
```
% 读入图像
img = imread('noisy_image.png');
% 对图像进行灰度化处理
gray_img = rgb2gray(img);
% 显示原图像
subplot(1,2,1);
imshow(gray_img);
title('Original Image');
% 添加高斯噪声
noisy_img = imnoise(gray_img, 'gaussian', 0.1);
% 显示加噪后的图像
subplot(1,2,2);
imshow(noisy_img);
title('Noisy Image');
% 计算巴特沃斯低通滤波器
D = 20; % 截止频率
n = 2; % 阶数
[b,a] = butter(n, D/(size(noisy_img,1)/2));
% 对图像进行滤波
filtered_img = filter(b, a, double(noisy_img));
% 显示滤波后的图像
figure;
subplot(1,2,1);
imshow(noisy_img);
title('Noisy Image');
subplot(1,2,2);
imshow(uint8(filtered_img));
title('Filtered Image');
```
其中,`butter` 函数用于计算巴特沃斯低通滤波器的系数,`filter` 函数用于对图像进行滤波。在此代码中,将截止频率设为 20,阶数设为 2,可以根据实际情况进行调整。
matlab均值滤波中值滤波理想低通滤波巴特沃斯低通滤波高斯高通滤波代码
### 回答1:
1. MATLAB均值滤波代码:
function output = meanFilter(image, windowSize)
[m, n] = size(image);
output = zeros(m, n);
for i = 1:m
for j = 1:n
sum = 0;
count = 0;
for k = i-floor(windowSize/2):i+floor(windowSize/2)
for l = j-floor(windowSize/2):j+floor(windowSize/2)
if (k > 0 && k <= m && l > 0 && l <= n)
sum = sum + image(k, l);
count = count + 1;
end
end
end
output(i, j) = sum / count;
end
end
end
2. MATLAB中值滤波代码:
function output = medianFilter(image, windowSize)
[m, n] = size(image);
output = zeros(m, n);
for i = 1:m
for j = 1:n
values = [];
for k = i-floor(windowSize/2):i+floor(windowSize/2)
for l = j-floor(windowSize/2):j+floor(windowSize/2)
if (k > 0 && k <= m && l > 0 && l <= n)
values = [values, image(k, l)];
end
end
end
output(i, j) = median(values);
end
end
end
3. 理想低通滤波代码:
function output = idealLowpassFilter(image, D0)
[m, n] = size(image);
output = zeros(m, n);
u = 0:(m-1);
v = 0:(n-1);
idx = find(u > m/2);
u(idx) = u(idx) - m;
idy = find(v > n/2);
v(idy) = v(idy) - n;
[V, U] = meshgrid(v, u);
D = sqrt(U.^2 + V.^2);
H = double(D <= D0);
F = fftshift(fft2(image));
output = real(ifft2(ifftshift(F .* H)));
end
4. 巴特沃斯低通滤波代码:
function output = butterworthLowpassFilter(image, D0, n)
[m, n] = size(image);
output = zeros(m, n);
u = 0:(m-1);
v = 0:(n-1);
idx = find(u > m/2);
u(idx) = u(idx) - m;
idy = find(v > n/2);
v(idy) = v(idy) - n;
[V, U] = meshgrid(v, u);
D = sqrt(U.^2 + V.^2);
H = 1 ./ (1 + ((D ./ D0).^(2*n)));
F = fftshift(fft2(image));
output = real(ifft2(ifftshift(F .* H)));
end
5. 高斯高通滤波代码:
function output = gaussianHighpassFilter(image, D0)
[m, n] = size(image);
output = zeros(m, n);
u = 0:(m-1);
v = 0:(n-1);
idx = find(u > m/2);
u(idx) = u(idx) - m;
idy = find(v > n/2);
v(idy) = v(idy) - n;
[V, U] = meshgrid(v, u);
D = sqrt(U.^2 + V.^2);
H = 1 - exp(-1 * (D.^2) / (2 * D0^2));
F = fftshift(fft2(image));
output = real(ifft2(ifftshift(F .* H)));
end
以上是MATLAB中实现均值滤波、中值滤波、理想低通滤波、巴特沃斯低通滤波和高斯高通滤波的代码。参数说明:image为输入图像,windowSize为滤波窗口的大小,D0为截止频率,n为巴特沃斯滤波器的阶数。输出结果为滤波后的图像。
### 回答2:
1. 均值滤波(Mean Filter):
function output = meanFilter(input, windowSize)
% 获取输入图像的大小
[height, width] = size(input);
% 创建输出图像
output = zeros(height, width);
% 定义窗口大小的一半
halfWindowSize = floor(windowSize / 2);
for i = halfWindowSize + 1 : height - halfWindowSize
for j = halfWindowSize + 1 : width - halfWindowSize
% 获取当前像素的邻域
neighborhood = input(i - halfWindowSize : i + halfWindowSize, j - halfWindowSize : j + halfWindowSize);
% 计算邻域内像素的平均值,并赋值给输出图像对应位置的像素
output(i, j) = mean(neighborhood(:));
end
end
end
2. 中值滤波(Median Filter):
function output = medianFilter(input, windowSize)
% 获取输入图像的大小
[height, width] = size(input);
% 创建输出图像
output = zeros(height, width);
% 定义窗口大小的一半
halfWindowSize = floor(windowSize / 2);
for i = halfWindowSize + 1 : height - halfWindowSize
for j = halfWindowSize + 1 : width - halfWindowSize
% 获取当前像素的邻域
neighborhood = input(i - halfWindowSize : i + halfWindowSize, j - halfWindowSize : j + halfWindowSize);
% 计算邻域内像素的中值,并赋值给输出图像对应位置的像素
output(i, j) = median(neighborhood(:));
end
end
end
3. 理想低通滤波(Ideal Lowpass Filter):
function output = idealLowpassFilter(input, cutoffFreq)
% 获取输入图像的大小和中心位置
[height, width] = size(input);
centerX = floor(width / 2) + 1;
centerY = floor(height / 2) + 1;
% 创建输出图像
output = zeros(height, width);
% 计算频域的网格
[X, Y] = meshgrid(1 : width, 1 : height);
% 计算频率坐标
freqX = X - centerX;
freqY = Y - centerY;
% 计算距离中心频率的距离
distance = sqrt(freqX.^2 + freqY.^2);
% 应用理想低通滤波器
output(distance <= cutoffFreq) = input(distance <= cutoffFreq);
end
4. 巴特沃斯低通滤波(Butterworth Lowpass Filter):
function output = butterworthLowpassFilter(input, cutoffFreq, order)
% 获取输入图像的大小和中心位置
[height, width] = size(input);
centerX = floor(width / 2) + 1;
centerY = floor(height / 2) + 1;
% 创建输出图像
output = zeros(height, width);
% 计算频域的网格
[X, Y] = meshgrid(1 : width, 1 : height);
% 计算频率坐标
freqX = X - centerX;
freqY = Y - centerY;
% 计算距离中心频率的距离
distance = sqrt(freqX.^2 + freqY.^2);
% 应用巴特沃斯低通滤波器
output = input .* (1 ./ (1 + (distance ./ cutoffFreq).^(2 * order)));
end
5. 高斯高通滤波(Gaussian Highpass Filter):
function output = gaussianHighpassFilter(input, sigma)
% 获取输入图像的大小和中心位置
[height, width] = size(input);
centerX = floor(width / 2) + 1;
centerY = floor(height / 2) + 1;
% 创建输出图像
output = zeros(height, width);
% 计算频域的网格
[X, Y] = meshgrid(1 : width, 1 : height);
% 计算频率坐标
freqX = X - centerX;
freqY = Y - centerY;
% 计算距离中心频率的距离
distance = sqrt(freqX.^2 + freqY.^2);
% 应用高斯高通滤波器
output = input .* (1 - exp(-(distance.^2) / (2 * sigma^2)));
end
### 回答3:
matlab中均值滤波、中值滤波、理想低通滤波、巴特沃斯低通滤波和高斯高通滤波的代码如下:
1. 均值滤波代码:
```matlab
% 均值滤波
function output = meanFilter(input, windowSize)
[m, n] = size(input);
output = zeros(m, n);
halfSize = floor(windowSize / 2);
for i = 1 + halfSize : m - halfSize
for j = 1 + halfSize : n - halfSize
% 取窗口内矩阵的均值
output(i, j) = mean2(input(i-halfSize:i+halfSize, j-halfSize:j+halfSize));
end
end
end
```
2. 中值滤波代码:
```matlab
% 中值滤波
function output = medianFilter(input, windowSize)
[m, n] = size(input);
output = zeros(m, n);
halfSize = floor(windowSize / 2);
for i = 1 + halfSize : m - halfSize
for j = 1 + halfSize : n - halfSize
% 取窗口内矩阵的中值
output(i, j) = median(input(i-halfSize:i+halfSize, j-halfSize:j+halfSize), 'all');
end
end
end
```
3. 理想低通滤波代码:
```matlab
% 理想低通滤波
function output = idealLowpassFilter(input, cutoffFrequency)
[m, n] = size(input);
output = ifftshift(input);
output = fft2(output);
% 构造理想低通滤波器
H = zeros(m, n);
for u = 1 : m
for v = 1 : n
D = sqrt((u - m/2)^2 + (v - n/2)^2);
if D <= cutoffFrequency
H(u, v) = 1;
end
end
end
% 与输入图像的傅里叶变换做点乘
output = output .* H;
output = abs(ifft2(output));
end
```
4. 巴特沃斯低通滤波代码:
```matlab
% 巴特沃斯低通滤波
function output = butterworthLowpassFilter(input, cutoffFrequency, n)
[m, n] = size(input);
output = ifftshift(input);
output = fft2(output);
% 构造巴特沃斯低通滤波器
H = zeros(m, n);
for u = 1 : m
for v = 1 : n
D = sqrt((u - m/2)^2 + (v - n/2)^2);
H(u, v) = 1 / (1 + (D / cutoffFrequency)^(2*n));
end
end
% 与输入图像的傅里叶变换做点乘
output = output .* H;
output = abs(ifft2(output));
end
```
5. 高斯高通滤波代码:
```matlab
% 高斯高通滤波
function output = gaussianHighpassFilter(input, cutoffFrequency)
[m, n] = size(input);
output = ifftshift(input);
output = fft2(output);
% 构造高斯高通滤波器
H = zeros(m, n);
for u = 1 : m
for v = 1 : n
D = sqrt((u - m/2)^2 + (v - n/2)^2);
H(u, v) = 1 - exp(-(D^2 / (2 * cutoffFrequency^2)));
end
end
% 与输入图像的傅里叶变换做点乘
output = output .* H;
output = abs(ifft2(output));
end
```
以上是一些简单的滤波方法的代码实现,只适用于二维的图像数据。具体的使用细节和参数调整可以根据实际情况进行修改。
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