我一直在阅读超分辨率图像重建主题，该领域的目的是从多个移位（亚像素）低分辨率（LR）图像创建高分辨率（HR）图像。以下代码从一张 HR 图像创建 4 个 LR 图像。然后使用非unifrom插值对高分辨率网格上的4个LR图像进行插值，以获取两侧比LR图像大4的HR图像。
主文件
im=double(imread('lena.bmp'));
figure,imshow(uint8(im)),title('original HR image');
shifts=[ 0, 0;
4.1, 2.68;
-3.7, 7.8;
-1.1, -6.5];
factor=4;
im1=create_low(im,shifts(1,1),shifts(1,2),factor);
im2=create_low(im,shifts(2,1),shifts(2,2),factor);
im3=create_low(im,shifts(3,1),shifts(3,2),factor);
im4=create_low(im,shifts(4,1),shifts(4,2),factor);
LR_images={im1,im2,im3,im4};
estimated_image = interpolate(LR_images,shifts,factor);
figure,imshow(uint8(estimated_image)),title('reconstructed image');
create_low.m 此函数创建 4 个 LR 图像。
function [ low ] = create_low(im,x_shift,y_shift,factor)
low = shift(im,x_shift,y_shift);
low=downsample(low,factor);
low=low';
low = downsample(low,factor);
low=low';
end
shift.m该函数通过线性插值进行亚像素移位。
interpolate.m将 4 个 LR 图像插值到 HR 网格上。
function rec = interpolate(s,shifts,factor)
n=length(s);
ss = size(s{1});
if (length(ss)==2) ss=[ss 1]; end
% compute the coordinates of the pixels from the N images.
for k=1:ss(3) % for each color channel
for i=1:n % for each image
s_c{i}=s{i}(:,:,k);
s_c{i} = s_c{i}(:);
r{i} = [1:factor:factor*ss(1)]'*ones(1,ss(2)); % create matrix with row indices
c{i} = ones(ss(1),1)*[1:factor:factor*ss(2)]; % create matrix with column indices
r{i} = r{i}+factor*shifts(i,2); %% the problem is here.
c{i} = c{i}+factor*shifts(i,1); %% the problem is here.
rn{i} = r{i}((r{i}>0)&(r{i}<=factor*ss(1))&(c{i}>0)&(c{i}<=factor*ss(2)));
cn{i} = c{i}((r{i}>0)&(r{i}<=factor*ss(1))&(c{i}>0)&(c{i}<=factor*ss(2)));
sn{i} = s_c{i}((r{i}>0)&(r{i}<=factor*ss(1))&(c{i}>0)&(c{i}<=factor*ss(2)));
end
s_ = []; r_ = []; c_ = []; sr_ = []; rr_ = []; cr_ = [];
for i=1:n % for each image
s_ = [s_; sn{i}];
r_ = [r_; rn{i}];
c_ = [c_; cn{i}];
end
clear s_c r c coord rn cn sn
% interpolate the high resolution pixels using cubic interpolation
rec_col = griddata(c_,r_,s_,[1:ss(2)*factor],[1:ss(1)*factor]','cubic');
rec(:,:,k) = reshape(rec_col,ss(1)*factor,ss(2)*factor);
end
rec(isnan(rec))=0;
我使用griddata了插值函数（三次），重建的图像太糟糕了，因为我认为“griddata”的参数值是错误的。如何纠正它们？
注意：当我更改此代码时
r{i} = r{i}+factor*shifts(i,2); %% the problem is here.
c{i} = c{i}+factor*shifts(i,1); %% the problem is here.
至
r{i} = r{i}-shifts(i,2); %% the problem is here.
c{i} = c{i}-shifts(i,1); %% the problem is here.
我得到了一个很好的图像，但我不知道为什么！
编辑
lena.bmp
