双边滤波器融合可见光和红外光的低频子带的代码 matlab
时间: 2024-06-04 13:11:44 浏览: 17
以下是一个示例代码,用于使用双边滤波器融合可见光和红外光的低频子带。该代码使用MATLAB编写。
% 读取可见光和红外光图像
visible_image = imread('visible_image.jpg');
infrared_image = imread('infrared_image.jpg');
% 将图像转换为灰度图像
visible_image_gray = rgb2gray(visible_image);
infrared_image_gray = rgb2gray(infrared_image);
% 计算可见光和红外光图像的低频子带
visible_image_low_freq = imgaussfilt(visible_image_gray, 5);
infrared_image_low_freq = imgaussfilt(infrared_image_gray, 5);
% 计算可见光和红外光图像的高频子带
visible_image_high_freq = visible_image_gray - visible_image_low_freq;
infrared_image_high_freq = infrared_image_gray - infrared_image_low_freq;
% 将可见光和红外光图像的低频子带进行双边滤波
visible_image_low_freq_filtered = bilateralFilter(visible_image_low_freq);
infrared_image_low_freq_filtered = bilateralFilter(infrared_image_low_freq);
% 将可见光和红外光图像的高频子带进行双边滤波
visible_image_high_freq_filtered = bilateralFilter(visible_image_high_freq);
infrared_image_high_freq_filtered = bilateralFilter(infrared_image_high_freq);
% 将可见光和红外光图像的低频子带和高频子带进行重组
visible_image_filtered = visible_image_low_freq_filtered + visible_image_high_freq_filtered;
infrared_image_filtered = infrared_image_low_freq_filtered + infrared_image_high_freq_filtered;
% 将可见光和红外光图像的重组结果进行融合
fused_image = imfuse(visible_image_filtered, infrared_image_filtered);
% 显示融合结果图像
imshow(fused_image);
% 定义双边滤波器函数
function [output_image] = bilateralFilter(input_image)
% 定义双边滤波器参数
spatial_sigma = 10;
range_sigma = 30;
% 获取输入图像的大小
[rows, cols] = size(input_image);
% 创建输出图像
output_image = zeros(rows, cols);
% 对于输入图像的每个像素
for i = 1:rows
for j = 1:cols
% 初始化权重总和和像素值总和
weight_sum = 0;
pixel_sum = 0;
% 对于输入图像的每个像素
for m = max(1, i - 2*spatial_sigma):min(rows, i + 2*spatial_sigma)
for n = max(1, j - 2*spatial_sigma):min(cols, j + 2*spatial_sigma)
% 计算空间距离和像素值距离
spatial_distance = sqrt((i - m)^2 + (j - n)^2);
range_distance = abs(input_image(i, j) - input_image(m, n));
% 计算像素的权重
weight = exp(-(spatial_distance^2)/(2*spatial_sigma^2)-(range_distance^2)/(2*range_sigma^2));
% 更新权重总和和像素值总和
weight_sum = weight_sum + weight;
pixel_sum = pixel_sum + weight*input_image(m, n);
end
end
% 计算输出像素值
output_image(i, j) = pixel_sum/weight_sum;
end
end
end
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