clear;clc; [image,pt,ft]=freadenvi('CSU_MS'); image=double(image); image1=zeros(488,310,3); for i=1:3 image1(:,:,i)=reshape(image(:,i),488,310); end times=2/0.5; J1=zeros(488*times,310*times,3); for i=1:3 J1(:,:,i)=imresize(image1(:,:,i),times,'bicubic'); end J2=Normalize3(J1);%归一化 0到1 double %转换成IHS IHS=rgbtoihs(J2(:,:,3:-1:1)); %将RGB变换到IHS空间，生成的IHS图像中，1通道对应H，2通道对应S，3通道对应I %读全色图像 输出pan 0-1double类型 [ori_pan,pt,ft]=freadenvi('CSU_PAN'); pan=zeros(1952,1240); pan=reshape(ori_pan,1952,1240)/max(ori_pan); %用pan代替IHS的I分量 IHS(:,:,3)=pan; %逆IHS变换，构建融合图象 fusion=ihstorgb(IHS); figure,imshow(Normalize3(image1(:,:,3:-1:1))); title('original') figure,imshow(fusion); title('IHS'); enviwrite(fusion,1952,1240,3,'C:\Users\Administrator\Documents\MATLAB\weiboyaogan_test4\fusion');

这段代码是一个图像融合的过程，具体步骤如下：

1. 读取一个多光谱图像，存储在变量image中。这个图像有三个通道，即RGB三个通道。然后将这个图像的每个通道的像素值reshape成一个二维矩阵，存储在变量image1中。
2. 对image1进行双三次插值，将图像的大小扩大一定的倍数，存储在变量J1中。
3. 对J1进行归一化，将像素值的范围限制在0到1之间，存储在变量J2中。
4. 将J2从RGB空间转换到IHS空间，其中1通道对应色调H，2通道对应饱和度S，3通道对应亮度I，存储在变量IHS中。
5. 读取一个全色图像，存储在变量ori_pan中。将其reshape成一个二维矩阵，并将像素值除以最大值，存储在变量pan中。
6. 将IHS中的I分量替换为pan，得到融合后的IHS图像fusion。
7. 将fusion从IHS空间转换回RGB空间，存储在变量fusion中。
8. 显示原始图像和融合后的图像，并将融合后的图像保存到本地。

需要注意的是，这里使用了一些函数，例如imresize()、rgbtoihs()、ihstorgb()等，这些函数用于图像的插值、颜色空间转换等操作。同时，还使用了freadenvi()和enviwrite()函数读取和写入ENVI格式的图像数据。

相关问题

clc clear all %% img_in = imread('ILSVRC2017_test_00000237.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) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) % 归一化至0-1 o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end改进该代码使其能在matlab上运行

clc;
clear all;

img_in = imread('ILSVRC2017_test_00000237.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);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Out_image = figure_normalize(In_image)
o_max_image = max(max(In_image));
o_min_image = min(min(In_image));
Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image);
end

clc; clear all; img_in = imread('ILSVRC2017_test_00000237.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); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end运行该代码报错未定义函数或变量 'figure_normalize'。

这个错误是因为你使用了一个名为`figure_normalize`的函数，但是你的代码中并没有定义这个函数。你需要自己编写这个函数并将其添加到代码中。

下面是一个可能的`figure_normalize`函数的示例，你可以将其添加到你的代码中：

function Out_image = figure_normalize(In_image)
o_max_image = max(max(In_image));
o_min_image = min(min(In_image));
Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image);
end

注意：如果你使用的是 MATLAB 的版本较新，可能会有现成的函数可以实现这个功能，例如`imadjust`函数。你可以查看 MATLAB 的文档进行了解。