压缩感知(十)

时间:2021-10-31 22:17:14

压缩感知仿真验证

一维信号重建实验

clear; 
close all;
 
choice_transform=1;       
choice_Phi=0;             
n = 512;
t = [0: n-1];
f = cos(2*pi/256*t)   sin(2*pi/128*t);   % 
n = length(f);
a = 0.2;                    
m = double(int32(a*n));

switch choice_transform
    case 1
        ft = dct(f);
        disp(ft = dct(f))
    case 0
        ft = fft(f);
        disp(ft = fft(f))
end
 
disp([ÐźÅÏ¡Êè¶È£º,num2str(length(find((abs(ft))>0.1)))])
figure(name, A Tone Time and Frequency Plot);
subplot(2, 1, 1);
plot(f);
xlabel(Time (s)); 
% ylabel(f(t));
subplot(2, 1, 2);
 
switch choice_transform
    case 1
        plot(ft)
        disp(plot(ft))
    case 0
        plot(abs(ft));
        disp(plot(abs(ft)))
end
xlabel(Frequency (Hz)); 
% ylabel(DCT(f(t)));

switch choice_Phi
    case 1
        Phi = PartHadamardMtx(m,n);       
    case 0
        Phi = sqrt(1/m) * randn(m,n);    
end
f2 = (Phi * f);       
% f2 = f(1:2:n);
 
switch choice_transform
    case 1
        Psi = dct(eye(n,n));           
        disp(Psi = dct(eye(n,n));)
    case 0
        Psi = inv(fft(eye(n,n)));      
        disp(Psi = inv(fft(eye(n,n)));)
end
 
A = Phi * Psi;                    % A = Phi * Psi
cvx_begin;
    variable x(n) complex;
%     variable x(n) ;
    minimize(norm(x,1));
    subject to
      A*x == f2;
cvx_end;
 
figure;
subplot(2,1,2);
switch choice_transform
    case 1
        plot(real(x));
        disp(plot(real(x)))
    case 0
        plot(abs(x));
        disp(plot(abs(x)))
end
 
title(Using L1 Norm£¨Frequency Domain£©);
 
%  ylabel(DCT(f(t))); xlabel(Frequency (Hz));
switch choice_transform
    case 1
        sig = dct(real(x));
        disp(sig = dct(real(x)))
    case 0
        sig = real(ifft(full(x)));
        disp(sig = real(ifft(full(x))))
end
subplot(2,1,1);
plot(f)
hold on;plot(sig);hold off
title(Using L1 Norm (Time Domain));
% ylabel(f(t)); xlabel(Time (s));
legend(Original,Recovery)

for K = 1:100
    theta = CS_OMP(f2,A,K);
    %     figure;plot(dct(theta));title([K=,num2str(K)])
    switch choice_transform
        case 1
            re(K) = norm(f-(dct(theta)));
        case 0
            re(K) = norm(f-real(ifft(full(theta))));
    end
end
theta = CS_OMP(f2,A,find(re==min(min(re))));
disp([×î¼ÑÏ¡Êè¶ÈK=,num2str(find(re==min(min(re))))]);
% theta = CS_OMP(f2,A,10);
figure;subplot(2,1,2);
switch choice_transform
    case 1
        plot(theta);
        disp(plot(theta))
    case 0
        plot(abs(theta));
        disp(plot(abs(theta)))
end
 
title([Using OMP(Frequence Domain)  K=,num2str(find(re==min(min(re))))])
 
switch choice_transform
    case 1
        sig2 = dct(theta);
        disp(sig2 = dct(theta))
    case 0
        sig2 = real(ifft(full(theta)));
        disp(sig2 = real(ifft(full(theta))))
end
 
subplot(2,1,1);plot(f);hold on;
plot(sig2)
hold off;
title([Using OMP(Time Domain)  K=,num2str(find(re==min(min(re))))]);
legend(Original,Recovery)

一维信号仿真结果

压缩感知(十) 

如上图所示,为原始信号f = cos(2*pi/256*t) sin(2*pi/128*t),及其频域图(频域稀疏)。

压缩感知(十)

取原信号的20%,使用L1范数算法对原信号进行恢复重建,效果如上图所示,蓝色为原始信号,红色为恢复信号。

压缩感知(十)

 取原信号的20%,使用OMP算法对原信号进行恢复重建,效果如上图所示,蓝色为原始信号,红色为恢复信号。

压缩感知(十)

取原信号的30%,使用L1范数算法对原信号进行恢复重建,效果如上图所示,蓝色为原始信号,红色为恢复信号。

压缩感知(十)

取原信号的30%,使用OMP算法对原信号进行恢复重建,效果如上图所示,蓝色为原始信号,红色为恢复信号。

小结:压缩感知原本就是为了信号(非图像)采集而生,所以在信号采集上有很强的实用性,甚至只需要原信号10~20%的信息,就可以复原出原信号的大部分特性。