【影像配準】基于粒子群改進的sift影像配準matlab原始碼-有解無憂

1 基于粒子群改進的sift影像配準

模型參考這里，

2 部分代碼

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close all;
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clear all;
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%% image path
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file_image='';
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%% read two images
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[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select reference image',...
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file_image);
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image_1=imread(strcat(pathname,filename));
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[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select the image to be registered',...
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file_image);
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image_2=imread(strcat(pathname,filename));
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%% Display the reference image and the image to be registered
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figure;
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subplot(1,2,1);
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imshow(image_1);
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title('Reference image');
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subplot(1,2,2);
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imshow(image_2);
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title('Image to be registered');
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%% make file for save images
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if (exist('save_image','dir')==0)%如果檔案夾不存在
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mkdir('save_image');
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end
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t1=clock;%Start time
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%% Convert input image format
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[~,~,num1]=size(image_1);
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[~,~,num2]=size(image_2);
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if(num1==3)
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image_11=rgb2gray(image_1);
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else
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image_11=image_1;
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end
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if(num2==3)
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image_22=rgb2gray(image_2);
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else
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image_22=image_2;
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end
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%Converted to floating point data between 0-1
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image_11=im2double(image_11);
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image_22=im2double(image_22);
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%% Define the constants used
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sigma=1.6;%Bottom Gauss Pyramid scale
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dog_center_layer=3;%Defines the DOG Pyramid intermediate layer, the default is 3
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contrast_threshold_1=0.04;%Contrast threshold of reference image
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contrast_threshold_2=0.04;%Contrast threshold of the image to be registered
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edge_threshold=10;%Edge threshold
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is_double_size=false;%Whether the image size is enlarged,the default is 'false',to get more points, set it to 'true'
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change_form='similarity';%Select geometric transformation type,it can be 'similarity','affine'
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%% The number of groups in Gauss Pyramid
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nOctaves_1=num_octaves(image_11,is_double_size);
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nOctaves_2=num_octaves(image_22,is_double_size);
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%% Generation of the first layer of the Gauss scale image
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image_11=create_initial_image(image_11,is_double_size,sigma);
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image_22=create_initial_image(image_22,is_double_size,sigma);
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%% Generating Gauss Pyramid of reference image
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tic;
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[gaussian_pyramid_1,gaussian_gradient_1,gaussian_angle_1]=...
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build_gaussian_pyramid(image_11,nOctaves_1,dog_center_layer,sigma);
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disp(['Reference image generation Gauss Pyramid spent time is：',num2str(toc),'s']);
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%% Generating DOG Pyramid of reference image
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tic;
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dog_pyramid_1=build_dog_pyramid(gaussian_pyramid_1,nOctaves_1,dog_center_layer);
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disp(['Reference image generation DOG Pyramid spent time is：',num2str(toc),'s']);
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clear gaussian_pyramid_1;
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%% Search for extreme points in the DOG Pyramid of the reference image
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tic;
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[key_point_array_1]=find_scale_space_extream...
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(...
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dog_pyramid_1,...
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nOctaves_1,...
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dog_center_layer,...
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contrast_threshold_1,...
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sigma,...
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edge_threshold,...
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gaussian_gradient_1,...
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gaussian_angle_1...
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);
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disp(['The extreme points of the reference image detection spend time is：',num2str(toc),'s']);
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clear dog_pyramid_1;
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%% The feature point descriptor generation,Reference image
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tic;
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[descriptors_1,locs_1]=calc_descriptors(gaussian_gradient_1,gaussian_angle_1,.....
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key_point_array_1,is_double_size);
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disp(['Reference image feature point descriptor generation spend time is：',num2str(toc),'s']);
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clear gaussian_gradient_1;
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clear gaussian_angle_1;
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%% Generating Gauss Pyramid of the image to be registered
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tic;
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[gaussian_pyramid_2,gaussian_gradient_2,gaussian_angle_2]=...
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build_gaussian_pyramid(image_22,nOctaves_2,dog_center_layer,sigma);
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disp(['The image to be registered generation Gauss Pyramid spent time is：',num2str(toc),'s']);
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%% Generating DOG Pyramid of the image to be registered
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tic;
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dog_pyramid_2=build_dog_pyramid(gaussian_pyramid_2,nOctaves_2,dog_center_layer);
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disp(['The image to be registered generation DOG Pyramid spent time is：：',num2str(toc),'s']);
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clear gaussian_pyramid_2;
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%% Search for extreme points int the DOG Pyramid of the image to be registered
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tic;
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[key_point_array_2]=find_scale_space_extream...
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(...
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dog_pyramid_2,...
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nOctaves_2,...
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dog_center_layer,...
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contrast_threshold_2,...
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sigma,...
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edge_threshold,...
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gaussian_gradient_2,...
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gaussian_angle_2...
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);
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disp(['The extreme points of the image to be registered detection spend time is：',num2str(toc),'s']);
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clear dog_pyramid_2;
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%% The feature point descriptor generation,the image to be registered
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tic;
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[descriptors_2,locs_2]=calc_descriptors(gaussian_gradient_2,gaussian_angle_2,...
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key_point_array_2,is_double_size);
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disp(['The image to be registered feature point descriptor generation spend time is：',num2str(toc),'s']);
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clear gaussian_gradient_2;
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clear gaussian_angle_2;
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%% Calculation of geometric transformation parameters
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tic;
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[solution,~,cor1,cor2]=...
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match(image_2, image_1,descriptors_2,locs_2,descriptors_1,locs_1,change_form);
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disp(['Feature point matching spend time is：',num2str(toc),'s']);
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tform=maketform('projective',solution');
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[M,N,P]=size(image_1);
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ff=imtransform(image_2,tform, 'XData',[1 N], 'YData',[1 M]);
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button=figure;
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subplot(1,2,1);
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imshow(image_1);
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title('Reference image');
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subplot(1,2,2);
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imshow(ff);
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title('Image after registration');
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str1=['.\save_image\','Results after registration','.jpg'];
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saveas(button,str1,'jpg');
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t2=clock;
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disp(['Total spending time is：',num2str(etime(t2,t1)),'s']);
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%% Display the detected feature points on the image
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[button1,button2]=showpoint_detected(image_1,image_2,locs_1,locs_2);
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str1=['.\save_image\','Reference image detection point','.jpg'];
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saveas(button1,str1,'jpg');
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str1=['.\save_image\','Points detected in the image to be registered','.jpg'];
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saveas(button2,str1,'jpg');
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%% Image fusion
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image_fusion(image_1,image_2,solution);