1 简介图像融合的方法很多,本文介绍的方法是基于主成分分析法(Principal Component Analysis)的可见光与红外图像的像素级融合.融合后的图像比原图具有更好的对比度.与直接重叠的融合方法相比,本文介绍的图像融合算法也具有更合适的对比度.因为本文所介绍的方法还利用了平滑滤波后的偏差信息,使图像的边缘更加清晰.2 部分代码% function Pca PCA(TM); clear g_R0; %r清晰度描述 g_G0; %g清晰度描述 g_B0; %b清晰度描述 h_R0; %熵的描述 h_G0; h_B0; fenzi_R0; fenzi_G0; fenzi_B0; fenmu_up_R0; fenmu_up_G0; fenmu_up_B0; fenmu_low_R0; fenmu_low_G0; fenmu_low_B0; init_up_R[]; init_up_G[]; init_up_B[]; init_low_R[]; init_low_G[]; init_low_B[]; upimread(high.jpg); %读图像 lowimread(low.jpg); figure(1) imshow(up); %读RGB数值 title(PCA-RGB表示的高分辨率图像); figure(2) imshow(low); title(PCA-RGB表示的低分辨率图像); [up_R]double(up(:,:,1)); [up_G]double(up(:,:,2)); [up_B]double(up(:,:,3)); [low_R]double(low(:,:,1)); [low_G]double(low(:,:,2)); [low_B]double(low(:,:,3)); [M,N,color]size(up); up_Mx 0; low_Mx0; for i 1 : M for j 1 : N up_S [up_R(i,j),up_G(i,j),up_B(i,j)]; % 生成由RG B组成的三维列向量 up_Mx up_Mx up_S; low_S [low_R(i,j),low_G(i,j),low_B(i,j)]; low_Mx low_Mx low_S; end end up_Mx up_Mx / (M*N); % 计算三维列向量的平均值 low_Mx low_Mx / (M*N); up_Cx 0; low_Cx0; for i 1 : M for j 1 : N up_S [up_R(i,j),up_G(i,j),up_B(i,j)]; up_Cx up_Cx up_S*up_S; low_S [low_R(i,j),low_G(i,j),low_B(i,j)]; low_Cx low_Cx low_S*low_S; end end up_Cx up_Cx / (M * N)- up_Mx*up_Mx; % 计算协方差矩陈 low_Cx low_Cx / (M * N)- low_Mx*low_Mx; [up_A,up_latent] eigs(up_Cx); % 协方差矩陈的特征向量组成的矩陈----PCA变换的系数矩陈,特征值 [low_A,low_latent] eigs(low_Cx); for i 1 : M for j 1 : N up_X [up_R(i,j),up_G(i,j),up_G(i,j)]; % 生成由RG B组成的三维列 up_Y up_A*up_X; % 每个象素点进行PCA变换正变换 up_Y up_Y; up_R(i,j) up_Y(1); % 高分辨率图片的第1主分量 up_G(i,j) up_Y(2); % 高分辨率图片的第2主分量 up_B(i,j) up_Y(3); % 高分辨率图片的第3主分量 low_X [low_R(i,j),low_G(i,j),low_G(i,j)]; low_Y low_A*low_X; low_Y low_Y; low_R(i,j) low_Y(1); % 低分辨率图片的第1主分量 low_G(i,j) low_Y(2); % 低分辨率图片的第2主分量 low_B(i,j) low_Y(3); % 低分辨率图片的第3主分量 end end for i 1 : M for j 1 : N up_Y [up_R(i,j),up_G(i,j),up_B(i,j)]; % 生成由RG B组成的三维列向量 up_X up_A*up_Y; % 每个象素点进行PCA变换反变换 up_X up_X; up_r(i,j) up_X(1); up_g(i,j) up_X(2); up_b(i,j) up_X(3); low_Y [up_R(i,j),low_G(i,j),low_B(i,j)]; low_X low_A*low_Y; low_X low_X; low_r(i,j) low_X(1); low_g(i,j) low_X(2); low_b(i,j) low_X(3); end end %RGB(:,:,1)up_r; %RGB(:,:,2)up_g; %RGB(:,:,3)up_b; RGB(:,:,1)low_r; RGB(:,:,2)low_g; RGB(:,:,3)low_b; figure(3) imshow(uint8(RGB)); title(PCA-RGB表示的转化图像); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 下面是计算相关系数 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% init_up_Rones(M,N)*mean(up_R(:)); init_up_Gones(M,N)*mean(up_G(:)); init_up_Bones(M,N)*mean(up_B(:)); init_low_Rones(M,N)*mean(low_R(:)); init_low_Gones(M,N)*mean(low_G(:)); init_low_Bones(M,N)*mean(low_B(:)); for i1:M for j1:N fenzi_Rfenzi_R(up_R(i,j)-init_up_R(i,j))*(low_R(i,j)-init_low_R(i,j)); fenmu_up_Rfenmu_up_R(up_R(i,j)-init_up_R(i,j))^2; fenmu_low_Rfenmu_low_R(low_R(i,j)-init_low_R(i,j))^2; fenzi_Gfenzi_G(up_R(i,j)-init_up_G(i,j))*(low_R(i,j)-init_low_G(i,j)); fenmu_up_Gfenmu_up_G(up_R(i,j)-init_up_G(i,j))^2; fenmu_low_Gfenmu_low_G(low_R(i,j)-init_low_G(i,j))^2; fenzi_Bfenzi_B(up_R(i,j)-init_up_B(i,j))*(low_R(i,j)-init_low_B(i,j)); fenmu_up_Bfenmu_up_B(up_R(i,j)-init_up_B(i,j))^2; fenmu_low_Bfenmu_low_B(low_R(i,j)-init_low_B(i,j))^2; end end rou_Rfenzi_R/(sqrt(fenmu_up_R*fenmu_low_R)); rou_Gfenzi_G/(sqrt(fenmu_up_G*fenmu_low_G)); rou_Bfenzi_B/(sqrt(fenmu_up_B*fenmu_low_B)); fprintf(\n\n R的相关系数为:%.4f\n G的相关系数为:%.4f\n B的相关系数为:%.4f,rou_R,rou_G,rou_B); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 下面是计算清晰度G % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for ii1:M-1 for jj1:N-1 g_Rg_Rsqrt((((low_r(ii1,jj)-low_r(ii,jj))^2(low_r(ii,jj1)-low_r(ii,jj))^2))/2); g_Gg_Gsqrt((((low_g(ii1,jj)-low_g(ii,jj))^2(low_g(ii,jj1)-low_g(ii,jj))^2))/2); g_Bg_Bsqrt((((low_b(ii1,jj)-low_b(ii,jj))^2(low_b(ii,jj1)-low_b(ii,jj))^2))/2); end end fprintf(\n\n R的清晰度为:%.4f\n G的清晰度为:%.4f\n B的清晰度为:%.4f,... g_R/(M-1)/(N-1),g_G/(M-1)/(N-1),g_B/(M-1)/(N-1));3 仿真结果4 参考文献[1]魏交统, 陈平, 潘晋孝. 基于主成分分析的递变能量X射线图像融合[J]. 中国体视学与图像分析, 2013(2):6.博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真相关matlab代码问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。
1 简介图像融合的方法很多,本文介绍的方法是基于主成分分析法(Principal Component Analysis)的可见光与红外图像的像素级融合.融合后的图像比原图具有更好的对比度.与直接重叠的融合方法相比,本文介绍的图像融合算法也具有更合适的对比度.因为本文所介绍的方法还利用了平滑滤波后的偏差信息,使图像的边缘更加清晰.2 部分代码% function Pca PCA(TM); clear g_R0; %r清晰度描述 g_G0; %g清晰度描述 g_B0; %b清晰度描述 h_R0; %熵的描述 h_G0; h_B0; fenzi_R0; fenzi_G0; fenzi_B0; fenmu_up_R0; fenmu_up_G0; fenmu_up_B0; fenmu_low_R0; fenmu_low_G0; fenmu_low_B0; init_up_R[]; init_up_G[]; init_up_B[]; init_low_R[]; init_low_G[]; init_low_B[]; upimread(high.jpg); %读图像 lowimread(low.jpg); figure(1) imshow(up); %读RGB数值 title(PCA-RGB表示的高分辨率图像); figure(2) imshow(low); title(PCA-RGB表示的低分辨率图像); [up_R]double(up(:,:,1)); [up_G]double(up(:,:,2)); [up_B]double(up(:,:,3)); [low_R]double(low(:,:,1)); [low_G]double(low(:,:,2)); [low_B]double(low(:,:,3)); [M,N,color]size(up); up_Mx 0; low_Mx0; for i 1 : M for j 1 : N up_S [up_R(i,j),up_G(i,j),up_B(i,j)]; % 生成由RG B组成的三维列向量 up_Mx up_Mx up_S; low_S [low_R(i,j),low_G(i,j),low_B(i,j)]; low_Mx low_Mx low_S; end end up_Mx up_Mx / (M*N); % 计算三维列向量的平均值 low_Mx low_Mx / (M*N); up_Cx 0; low_Cx0; for i 1 : M for j 1 : N up_S [up_R(i,j),up_G(i,j),up_B(i,j)]; up_Cx up_Cx up_S*up_S; low_S [low_R(i,j),low_G(i,j),low_B(i,j)]; low_Cx low_Cx low_S*low_S; end end up_Cx up_Cx / (M * N)- up_Mx*up_Mx; % 计算协方差矩陈 low_Cx low_Cx / (M * N)- low_Mx*low_Mx; [up_A,up_latent] eigs(up_Cx); % 协方差矩陈的特征向量组成的矩陈----PCA变换的系数矩陈,特征值 [low_A,low_latent] eigs(low_Cx); for i 1 : M for j 1 : N up_X [up_R(i,j),up_G(i,j),up_G(i,j)]; % 生成由RG B组成的三维列 up_Y up_A*up_X; % 每个象素点进行PCA变换正变换 up_Y up_Y; up_R(i,j) up_Y(1); % 高分辨率图片的第1主分量 up_G(i,j) up_Y(2); % 高分辨率图片的第2主分量 up_B(i,j) up_Y(3); % 高分辨率图片的第3主分量 low_X [low_R(i,j),low_G(i,j),low_G(i,j)]; low_Y low_A*low_X; low_Y low_Y; low_R(i,j) low_Y(1); % 低分辨率图片的第1主分量 low_G(i,j) low_Y(2); % 低分辨率图片的第2主分量 low_B(i,j) low_Y(3); % 低分辨率图片的第3主分量 end end for i 1 : M for j 1 : N up_Y [up_R(i,j),up_G(i,j),up_B(i,j)]; % 生成由RG B组成的三维列向量 up_X up_A*up_Y; % 每个象素点进行PCA变换反变换 up_X up_X; up_r(i,j) up_X(1); up_g(i,j) up_X(2); up_b(i,j) up_X(3); low_Y [up_R(i,j),low_G(i,j),low_B(i,j)]; low_X low_A*low_Y; low_X low_X; low_r(i,j) low_X(1); low_g(i,j) low_X(2); low_b(i,j) low_X(3); end end %RGB(:,:,1)up_r; %RGB(:,:,2)up_g; %RGB(:,:,3)up_b; RGB(:,:,1)low_r; RGB(:,:,2)low_g; RGB(:,:,3)low_b; figure(3) imshow(uint8(RGB)); title(PCA-RGB表示的转化图像); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 下面是计算相关系数 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% init_up_Rones(M,N)*mean(up_R(:)); init_up_Gones(M,N)*mean(up_G(:)); init_up_Bones(M,N)*mean(up_B(:)); init_low_Rones(M,N)*mean(low_R(:)); init_low_Gones(M,N)*mean(low_G(:)); init_low_Bones(M,N)*mean(low_B(:)); for i1:M for j1:N fenzi_Rfenzi_R(up_R(i,j)-init_up_R(i,j))*(low_R(i,j)-init_low_R(i,j)); fenmu_up_Rfenmu_up_R(up_R(i,j)-init_up_R(i,j))^2; fenmu_low_Rfenmu_low_R(low_R(i,j)-init_low_R(i,j))^2; fenzi_Gfenzi_G(up_R(i,j)-init_up_G(i,j))*(low_R(i,j)-init_low_G(i,j)); fenmu_up_Gfenmu_up_G(up_R(i,j)-init_up_G(i,j))^2; fenmu_low_Gfenmu_low_G(low_R(i,j)-init_low_G(i,j))^2; fenzi_Bfenzi_B(up_R(i,j)-init_up_B(i,j))*(low_R(i,j)-init_low_B(i,j)); fenmu_up_Bfenmu_up_B(up_R(i,j)-init_up_B(i,j))^2; fenmu_low_Bfenmu_low_B(low_R(i,j)-init_low_B(i,j))^2; end end rou_Rfenzi_R/(sqrt(fenmu_up_R*fenmu_low_R)); rou_Gfenzi_G/(sqrt(fenmu_up_G*fenmu_low_G)); rou_Bfenzi_B/(sqrt(fenmu_up_B*fenmu_low_B)); fprintf(\n\n R的相关系数为:%.4f\n G的相关系数为:%.4f\n B的相关系数为:%.4f,rou_R,rou_G,rou_B); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 下面是计算清晰度G % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for ii1:M-1 for jj1:N-1 g_Rg_Rsqrt((((low_r(ii1,jj)-low_r(ii,jj))^2(low_r(ii,jj1)-low_r(ii,jj))^2))/2); g_Gg_Gsqrt((((low_g(ii1,jj)-low_g(ii,jj))^2(low_g(ii,jj1)-low_g(ii,jj))^2))/2); g_Bg_Bsqrt((((low_b(ii1,jj)-low_b(ii,jj))^2(low_b(ii,jj1)-low_b(ii,jj))^2))/2); end end fprintf(\n\n R的清晰度为:%.4f\n G的清晰度为:%.4f\n B的清晰度为:%.4f,... g_R/(M-1)/(N-1),g_G/(M-1)/(N-1),g_B/(M-1)/(N-1));3 仿真结果4 参考文献[1]魏交统, 陈平, 潘晋孝. 基于主成分分析的递变能量X射线图像融合[J]. 中国体视学与图像分析, 2013(2):6.博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真相关matlab代码问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。
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