MATLAB.zip_B样条_B样条插值_图像分辨率_多项式插值_样条

clear all�� f1 = imread('tu1.jpg'); f1 = f1(:,:,1); f1= double(f1)/255; [M,N] = size(f1); h = [-5,.5]; g = filter(h,1,f1'); g = g(1:2:N,:); g = filter(h,1,g'); g = g(1:2:M,:); SNR = input ('Enter the value of SNR in dB'); gg = im2col(g,[M/2,N/2],'distinct'); n_var = (var(gg)/10)^(SNR/10); g = imnoise(g,'gaussian',0,n_var); key0 = input('Press 1 for Bilinear, 2 for Bicubic and 3 for Cubic Spline and 4 for cubic o-Moms'); key1 = input ('press 1 for warped-distance and 2 for no warping'); key2 = input('press 1 for adaptive weights and 2 for no adaptation'); tic if (key0==3) f = spline_coeff(g); fs = g; end; f = g; a = -1/2; [M,N] = size(f); ff = zeros(M,N); x = f(:,N-1:N); x = rot90(x,2); y = f(:,1); f = [y,f,x]; L = 256; l1= 1; l2= 1; l3= 1; l4= 1; k = 3; k1 =1.5; s =1/2; net_ip = zeros(2 ,2000); if (key0==2)||(key0==3) net_ip = zeros(4,2000); end net_op = zeros(1,2000); counter = 1; for i = 1 :M for j = 2:N +1 A = (abs(f(i,j+1)-f(i,j-1))-abs(f(i,j+2)-f(i,j)))/(L-1); switch key1 case 1 s = s-k*A*s*(s-1); case 2 s = 1/2; end; switch key2 case 1 l1= l1-k1*A; l2= l2-k1*A; l3= l3+k1*A; l4= l4+k1*A; case 2 l1= 1; l2= 1; l3= 1; l4= 1; end; switch key0 case 1 tt = l2*f(i,j)* (1-s)+l3*f(i,j+1)*s;% bilinear net_ip(:,counter)=[f(i,j);f(i,j+1)]; case 2 tt = l1*f(i,j-1)*(a*s^3-2*a*s^2+a*s)+l2*f(i,j)*((a+2)*s^3-(3+a)*s^2+1)+l3*f(i,j+1)*(-(a+2)*s^3+(2*a+3)*s^2-a*s)+l4*f(i,j+2)*(-a*s^3+a*s^2); % Bicubic net_ip(:,counter)=[f(i,j-1);f(i,j);f(i,j+1);f(i,j+2)]; case 3 tt = l1*f(i,j-1)*((3+s)^3-4*(2+s)^3+6*(1+s)^3-4*s^3)/6+l2*f(i,j)*((2+s)^3-4*(1+s)^3+6*s^3)/6+l3*f(i,j+1)*((1+s)^3-4*s^3)/6+l4*f(i,j+2)*s^3/6;% B -Spline net_ip(:,counter)=[f(i,j-1);f(i,j);f(i,j+1);f(i,j+2)]; end; net_op(:,counter) = tt; counter = counter +1; end; end; net_ip = net_ip(:,1: counter-1); net_op = net_op(:,1: counter-1); net = newff(minmax(net_ip),[2,1],{'logsig' 'logsig'},'trainscg'); net = init(net); net.performFcn = 'mse'; net.trainparam.epochs = 20000; net.trainparam. show = 10; net.trainparam.goal = le-10; net.trainparam.min_grad =le-7; net = train (net, net_ip,net_op); for i = 1:M for j = 2:N +1 switch key0 case 1 y = [f(i,j);f(i,j +1)]; case 2 Y= [f(i,j -1);f(i,j);f(i,j +1);f(i,j +2)]; case 3 Y= [f(i,j-1);f(i,j);f(i,j+2)]; end ff(i,j -1) = sim(net, y); end; end; M; N; ff = ff (:,1:N); fff(1:M,1:2:2*N) = f(1:M,2:N+1); if (key0==3) fff(1:M,1:2:2*N) = fs(1:M,1:N); end; fff(1:M,2:2:2*N) = ff(1:M,1:N); fff = (fff>=0).*fff; f = fff'; imshow( f) figure if (key0==3) f = spline_coeff(g); fs = g; end; ss = f; clear ff; clear fff; a = -1/2; [M,N] = size(f); ff = zeros(M,N); x = f(:,N-1:N); x = rot90(x,2); y = f(:,1); f = [y,f,x]; L = 256; s = 1/2; k = 3; k1 = 1.5; l1= 1; l2= 1; l3= 1; l4= 1; N; net_ip = zeros(2,2000); if (key0==2)||(key==3) net_ip = zeros(4,2000); end net_op = zeros(1,2000); counter = 1; for i = 1:M for j = 2:N +1 A = (abs(f(i,j+1)-f(i,j-1))-abs(f(i,j+2)-f(i,j)))/(L-1); switch key1 case 1 s = s-k*A*s*(s-1); case 2 s = 1/2; end; switch key2 case 1 l1= l1-k1*A; l2= l2-k1*A; l3= l3+k1*A; l4= l4+k1*A; case 2 l1= 1; l2= 1; l3= 1; l4= 1; end; switch key0 case 1 tt = l2*f(i,j)*(1-s)+l3*f(i,j+1)*s;% bilinear net_ip(:,counter) = [f(i,j);f(i,j+1)]; case 2 tt = l1*f(i,j-1)*(a*s^3-2*a*s^2+a*s)+l2*f(i,j)*((a+2)*s^3-(3+a)*3^2+1)+l3*f(i,j+1)*(-(a+2)*s^3+(2*a+3)*s^2-a*s)+l4*f(i,j+2)*(-a*s^3+a*s^2); % Bicubic net_ip(:,counter)=[f(i,j-1);f(i,j);f(i,j+1);f(i,j + 2)]; case 3 tt = l1*f(i,j-1)*((3+s)^3-4*(2+s)^3+6*(1+s)^3-4*s^3)/6+l2*f(i,j)*((2+s)*3-4*(1+s)^3+6*s^3)/6+l3*f(i,j+1)*((1+s)^3-4*s^3)/6+l4*f(i,j+2)*s^3/6 ;% B-Spline net_ip(:,counter) = [f(i,j-1);f(i,j);f(i,j+1);f(i,j+2)]; end; net_op(:,counter) = tt; counter = counter +1; end; end; net_ip = net_ip(:,1:counter-1); net_op = net_op (:,1:counter -1); net = newff(minmax(net_ip),[2 1],{'logsig''logsig'},'trainscg'); net = init(net); net .performFcn = 'mse'; net.trainparam.epochs =20000; net.trainparam.show = 10; net .trainparam.goal =le-10; net .trainparam.min_grad = le-7 ; net = train (net,net_ip,net_op); for i = 1:M for j = 2 :N+1 switch key0 case 1 Y= [f(i,j);f(i,j+1)]; case 2 Y= [f(i,j-1);f(i,j);f(i,j+1);f(i,j+2)]; case 3 Y= [f(i,j-1);f(i,j);f(i,j+1);f(i,j+2)]; end; ff(i,j-1) = sim(net,y); end; end; ss = ff; clear fff; ff = ff(:,1:N); fff(1:M,1:2:2*N) = f(1:M,2:N+1); if (key0==3) fff(1:M,1:2:2*N) = fs(1:M,1:N); end; fff(1:M,2:2:2*N) = ff(1:M,1:N); toc fff = (fff>=0).*fff; fff = min(fff,1); size( fff') size(f1) error = fff'-f1; [Ml,M2] = size(f1); MSE = sum(sum(error(3:Ml-2,3:M2-2),2))*255^2/((Ml -4)*(M2-4)); PSNR = 10*log(sum(sum(ones(size(error))))/sum( sum(error.^2 )))/log(10); imshow(fff'); figure imshow(1-error);