高斯滤波 clear,clc; % 读取图像 Img = imread('t1.jpg'); M = size(Img); if numel(M)>2 gray = rgb2gray(Img); else gray = Img; end % 创建滤波器 W = fspecial('gaussian',[5,5],1); G = imfilter(gray, W, 'replicate'); figure(1); subplot(121); imshow(gray); title('原始图像'); subplot(122); imshow(G); title('滤波后图像');中为什么用W = fspecial('gaussian',[5,5],1);

在高斯滤波中，使用高斯核对图像进行卷积操作可以实现平滑图像的目的。高斯核的大小和方差会影响滤波效果，一般情况下，高斯核的大小应该选取奇数大小，方差越大滤波效果越弱。在这段代码中， fspecial('gaussian',[5,5],1) 创建了一个 $5\times5$ 的高斯核，方差为 1，用于对灰度图像进行平滑处理。

相关问题

补全代码：clear;clc % 读取图像文件 img = imread('Lena01.bmp'); img =imresize(img,0.2); % 显示原始图像 subplot(2, 2, 1); imshow(img); title('Original Image'); % 计算傅里叶变换 f = double(img); F = zeros(size(f)); [M, N] = size(f); for u = 0:M-1 for v = 0:N-1 for x = 0:M-1 for y = 0:N-1 end end end end % 显示傅里叶变换后的图像 subplot(2, 2, 2); imshow(log(1+abs(F)), []); title('Fourier Transform');

补全代码如下：

clear;clc % 读取图像文件
img = imread('Lena01.bmp');
img =imresize(img,0.2);

% 显示原始图像
subplot(2, 2, 1);
imshow(img);
title('Original Image');

% 计算傅里叶变换
f = double(img);
F = zeros(size(f));
[M, N] = size(f);
for u = 0:M-1
for v = 0:N-1
for x = 0:M-1
for y = 0:N-1
F(u+1, v+1) = F(u+1, v+1) + f(x+1, y+1) * exp(-2j*pi*((u*x/M)+(v*y/N)));
end
end
end
end

% 显示傅里叶变换后的图像
subplot(2, 2, 2);
imshow(log(1+abs(F)), []);
title('Fourier Transform');

clc clear all %% img_in = imread('0066.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %% %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('name','滤波') imshow(img_blur) img_lab_blur = rgb2lab(img_blur); %% %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg') figure imshow(Sd_normalized) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end运行代码时报错错误: 非法使用保留关键字 "end"。改进代码

代码中的错误是因为您没有正确定义 `figure_normalize` 函数，导致程序无法执行。此外，您应该将 `end` 改为 `endfor` 或 `endfunction`，以正确结束循环或函数定义。以下是改进后的代码：

clc 
clear all 

img_in = imread('0066.jpg'); 
m = size(img_in,1); 
n = size(img_in,2); 

img_lab = rgb2lab(img_in); 
img_L_mean = mean(mean(img_lab(:,:,1))); 
img_a_mean = mean(mean(img_lab(:,:,2))); 
img_b_mean = mean(mean(img_lab(:,:,3))); 

%% %高斯滤波 
img_R = img_in(:,:,1); 
img_G = img_in(:,:,2); 
img_B = img_in(:,:,3); 
w = fspecial('gaussian',[7 7]); 
img_R_blur = imfilter(img_R,w); 
img_G_blur = imfilter(img_G,w); 
img_B_blur = imfilter(img_B,w); 
img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); 

figure('name','滤波') 
imshow(img_blur) 

img_lab_blur = rgb2lab(img_blur); 

%% %计算显著图 
Sd = zeros(m,n); 
for i = 1:m    
    for j = 1:n        
        Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2);    
    endfor 
endfor 

%归一化 
Sd_normalized = normalize(Sd); 
imwrite(Sd_normalized,'FT_saliency.jpg') 
figure 
imshow(Sd_normalized) 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

function out = normalize(in)
    out = (in - min(in(:))) ./ (max(in(:)) - min(in(:)));
endfunction