skr2_regular.m

super resolution matlab

function [z, zx1, zx2] = skr2_regular(y, h, C, r, ksize)
% [SKR2_REGULAR]
% The second order steering kernel regression function for regularly sampled
% data.
%
% [USAGE]
% [z, zx1, zx2] = skr2_regular(y, h, C, r, ksize)
%
% [RETURNS]
% z     : the estimated image
% zx1   : the estimated gradient image along the x1 direction (vertical
%        direction)
% zx2   : the estimated gradient image along the x2 direction (horizontal
%        direction)
%
% [PARAMETERS]
% y     : the input image
% h     : the global smoothing parameter
% C     : the covariance matrices containing local orientation information
% r     : the upscaling factor ("r" must be an integer number)
% ksize : the size of the kernel (ksize x ksize, and "ksize" must be
%         an odd number)
%
% [HISTORY]
% June 16, 2007 : created by Hiro

% Get the oritinal image size
[N, M] = size(y);

% Initialize the return parameters
z = zeros(N*r, M*r);
zx1 = zeros(N*r, M*r);
zx2 = zeros(N*r, M*r);

% Pixel sampling positions
radius = (ksize - 1) / 2;
[x2, x1] = meshgrid(-radius-(r-1)/r : 1/r : radius, -radius-(r-1)/r : 1/r : radius);

% pre-culculation for covariance matrices
C11 = zeros(N, M);
C12 = zeros(N, M);
C22 = zeros(N, M);
sq_detC = zeros(N, M);
for n = 1 : N
    for m = 1 : M
        C11(n,m) = C(1,1,n,m);
        C12(n,m) = C(1,2,n,m);
        C22(n,m) = C(2,2,n,m);
        sq_detC(n,m) = sqrt(det(C(:,:,n,m)));
    end
end

% Mirroring
y = EdgeMirror(y, [radius, radius]);
C11 = EdgeMirror(C11, [radius, radius]);
C12 = EdgeMirror(C12, [radius, radius]);
C22 = EdgeMirror(C22, [radius, radius]);
sq_detC = EdgeMirror(sq_detC, [radius, radius]);

% Estimate an image and its first gradients with pixel-by-pixel
for i = 1 : r
    for j = 1 : r
        xx1 = downsample2(x1(r-i+1:end, r-j+1:end), r);
        xx2 = downsample2(x2(r-i+1:end, r-j+1:end), r);
        
        % The feture matrix
        Xx = [ones(ksize^2,1), xx1(:), xx2(:), xx1(:).^2, xx1(:).*xx2(:), xx2(:).^2];
                
        for n = 1 : N
            nn = (n - 1) * r + i;
            for m = 1 : M
                mm = (m - 1) * r + j;
                
                % Neighboring samples to be taken account into the estimation
                yp = y(n:n+ksize-1, m:m+ksize-1);
                
                % compute the weight matrix
                tt = xx1 .* (C11(n:n+ksize-1, m:m+ksize-1) .* xx1...
                        + C12(n:n+ksize-1, m:m+ksize-1) .* xx2)...
                        + xx2 .* (C12(n:n+ksize-1, m:m+ksize-1) .* xx1...
                        + C22(n:n+ksize-1, m:m+ksize-1) .* xx2);
                W = exp(-(0.5/h^2) * tt) .* sq_detC(n:n+ksize-1, m:m+ksize-1);
                
                % Equivalent kernel
                Xw = [Xx(:,1).*W(:), Xx(:,2).*W(:), Xx(:,3).*W(:),...
                        Xx(:,4).*W(:), Xx(:,5).*W(:), Xx(:,6).*W(:)];
                A = inv(Xx.' * Xw + eye(6)*0.0000001) * (Xw.');
                
                % Estimate the pixel values at (nn,mm)
                z(nn,mm)   = A(1,:) * yp(:);
                zx1(nn,mm) = A(2,:) * yp(:);
                zx2(nn,mm) = A(3,:) * yp(:);
            end
        end
    end
end