【图像配准】基于粒子群改进的sift图像配准matlab源码
作者：互联网
1 基于粒子群改进的sift图像配准
模型参考这里。
2 部分代码
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close all;
clear all;
%% image path
file_image='';
%% read two images
[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select reference image',...
file_image);
image_1=imread(strcat(pathname,filename));
[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select the image to be registered',...
file_image);
image_2=imread(strcat(pathname,filename));
%% Display the reference image and the image to be registered
figure;
subplot(1,2,1);
imshow(image_1);
title('Reference image');
subplot(1,2,2);
imshow(image_2);
title('Image to be registered');
%% make file for save images
if (exist('save_image','dir')==0)%如果文件夹不存在
mkdir('save_image');
end
t1=clock;%Start time
%% Convert input image format
[~,~,num1]=size(image_1);
[~,~,num2]=size(image_2);
if(num1==3)
image_11=rgb2gray(image_1);
else
image_11=image_1;
end
if(num2==3)
image_22=rgb2gray(image_2);
else
image_22=image_2;
end
%Converted to floating point data between 0-1
image_11=im2double(image_11);
image_22=im2double(image_22);
%% Define the constants used
sigma=1.6;%Bottom Gauss Pyramid scale
dog_center_layer=3;%Defines the DOG Pyramid intermediate layer, the default is 3
contrast_threshold_1=0.04;%Contrast threshold of reference image
contrast_threshold_2=0.04;%Contrast threshold of the image to be registered
edge_threshold=10;%Edge threshold
is_double_size=false;%Whether the image size is enlarged,the default is 'false',to get more points, set it to 'true'
change_form='similarity';%Select geometric transformation type,it can be 'similarity','affine'
%% The number of groups in Gauss Pyramid
nOctaves_1=num_octaves(image_11,is_double_size);
nOctaves_2=num_octaves(image_22,is_double_size);
%% Generation of the first layer of the Gauss scale image
image_11=create_initial_image(image_11,is_double_size,sigma);
image_22=create_initial_image(image_22,is_double_size,sigma);
%% Generating Gauss Pyramid of reference image
tic;
[gaussian_pyramid_1,gaussian_gradient_1,gaussian_angle_1]=...
build_gaussian_pyramid(image_11,nOctaves_1,dog_center_layer,sigma);
disp(['Reference image generation Gauss Pyramid spent time is：',num2str(toc),'s']);
%% Generating DOG Pyramid of reference image
tic;
dog_pyramid_1=build_dog_pyramid(gaussian_pyramid_1,nOctaves_1,dog_center_layer);
disp(['Reference image generation DOG Pyramid spent time is：',num2str(toc),'s']);
clear gaussian_pyramid_1;
%% Search for extreme points in the DOG Pyramid of the reference image
tic;
[key_point_array_1]=find_scale_space_extream...
(...
dog_pyramid_1,...
nOctaves_1,...
dog_center_layer,...
contrast_threshold_1,...
sigma,...
edge_threshold,...
gaussian_gradient_1,...
gaussian_angle_1...
);
disp(['The extreme points of the reference image detection spend time is：',num2str(toc),'s']);
clear dog_pyramid_1;
%% The feature point descriptor generation,Reference image
tic;
[descriptors_1,locs_1]=calc_descriptors(gaussian_gradient_1,gaussian_angle_1,.....
key_point_array_1,is_double_size);
disp(['Reference image feature point descriptor generation spend time is：',num2str(toc),'s']);
clear gaussian_gradient_1;
clear gaussian_angle_1;
%% Generating Gauss Pyramid of the image to be registered
tic;
[gaussian_pyramid_2,gaussian_gradient_2,gaussian_angle_2]=...
build_gaussian_pyramid(image_22,nOctaves_2,dog_center_layer,sigma);
disp(['The image to be registered generation Gauss Pyramid spent time is：',num2str(toc),'s']);
%% Generating DOG Pyramid of the image to be registered
tic;
dog_pyramid_2=build_dog_pyramid(gaussian_pyramid_2,nOctaves_2,dog_center_layer);
disp(['The image to be registered generation DOG Pyramid spent time is：：',num2str(toc),'s']);
clear gaussian_pyramid_2;
%% Search for extreme points int the DOG Pyramid of the image to be registered
tic;
[key_point_array_2]=find_scale_space_extream...
(...
dog_pyramid_2,...
nOctaves_2,...
dog_center_layer,...
contrast_threshold_2,...
sigma,...
edge_threshold,...
gaussian_gradient_2,...
gaussian_angle_2...
);
disp(['The extreme points of the image to be registered detection spend time is：',num2str(toc),'s']);
clear dog_pyramid_2;
%% The feature point descriptor generation,the image to be registered
tic;
[descriptors_2,locs_2]=calc_descriptors(gaussian_gradient_2,gaussian_angle_2,...
key_point_array_2,is_double_size);
disp(['The image to be registered feature point descriptor generation spend time is：',num2str(toc),'s']);
clear gaussian_gradient_2;
clear gaussian_angle_2;
%% Calculation of geometric transformation parameters
tic;
[solution,~,cor1,cor2]=...
match(image_2, image_1,descriptors_2,locs_2,descriptors_1,locs_1,change_form);
disp(['Feature point matching spend time is：',num2str(toc),'s']);
tform=maketform('projective',solution');
[M,N,P]=size(image_1);
ff=imtransform(image_2,tform, 'XData',[1 N], 'YData',[1 M]);
button=figure;
subplot(1,2,1);
imshow(image_1);
title('Reference image');
subplot(1,2,2);
imshow(ff);
title('Image after registration');
str1=['.\save_image\','Results after registration','.jpg'];
saveas(button,str1,'jpg');
t2=clock;
disp(['Total spending time is：',num2str(etime(t2,t1)),'s']);
%% Display the detected feature points on the image
[button1,button2]=showpoint_detected(image_1,image_2,locs_1,locs_2);
str1=['.\save_image\','Reference image detection point','.jpg'];
saveas(button1,str1,'jpg');
str1=['.\save_image\','Points detected in the image to be registered','.jpg'];
saveas(button2,str1,'jpg');
%% Image fusion
image_fusion(image_1,image_2,solution);
3 仿真结果
4 参考文献
[1]冯林, 张名举, 贺明峰,等. 用改进的粒子群算法实现多模态刚性医学图像的配准[J]. 计算机辅助设计与图形学学报, 2004(09):1269-1274.
5 代码下载
获取代码方式1：
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标签：...,配准,%%,image,pyramid,gaussian,dog,源码,图像 来源： https://blog.csdn.net/qq_59747472/article/details/120587285