Hi chums, I am trying to turn a matlab script into C, and have a problem with one line: Where A is a matrix that m~=n and b is a column matrix of proportions m,1.
The full script I am trying to convert (and have up until that point) is:
Code:
%
% gsolve.m − Solve for imaging system response function
%
% Given a set of pixel values observed for several pixels in several
% images with different exposure times, this function returns the
% imaging system’s response function g as well as the log film irradiance
% values for the observed pixels.
%
% Assumes:
%
% Zmin = 0
% Zmax = 255
%
% Arguments:
%
% Z(i,j) is the pixel values of pixel location number i in image j
% B(j) is the log delta t, or log shutter speed, for image j
% l is lamdba, the constant that determines the amount of smoothness
% w(z) is the weighting function value for pixel value z
%
% Returns:
%
% g(z) is the log exposure corresponding to pixel value z
% lE(i) is the log film irradiance at pixel location i
%
function [g,lE]=gsolve(Z,B,l,w)
n = 256;
A = zeros(size(Z,1)*size(Z,2)+n+1,n+size(Z,1));
b = zeros(size(A,1),1);
%% Include the data−fitting equations
k = 1;
for i=1:size(Z,1)
for j=1:size(Z,2)
wij = w(Z(i,j)+1);
A(k,Z(i,j)+1) = wij; A(k,n+i) = −wij; b(k,1) = wij * B(i,j);
k=k+1;
end
end
%% Fix the curve by setting its middle value to 0
A(k,129) = 1;
k=k+1;
%% Include the smoothness equations
for i=1:n−2
A(k,i)=l*w(i+1); A(k,i+1)=−2*l*w(i+1); A(k,i+2)=l*w(i+1);
k=k+1;
end
%% Solve the system using SVD
x = A\b;
g = x(1:n);
lE = x(n+1:size(x,1));
It is used to generate the response function, using SVD (single value decomposition), of a digital image process by finding the pixel value difference over a set number of known exposure images.
Matlab help defines '\' as:
Code:
\ Backslash or matrix left division. If A is a square matrix, A\B is
roughly the same as inv(A)*B, except it is computed in a different
way. If A is an n-by-n matrix and B is a column vector with n
components, or a matrix with several such columns, then X = A\B is
the solution to the equation AX = B computed by Gaussian
elimination. A warning message is displayed if A is badly scaled or
nearly singular. See the reference page for mldivide for more
information.
If A is an m-by-n matrix with m ~= n and B is a column vector
with m components, or a matrix with several such columns, then X =
A\B is the solution in the least squares sense to the under- or
overdetermined system of equations AX = B. The effective rank, k, of
A is determined from the QR decomposition with pivoting (see
Algorithm for details). A solution X is computed that has at most k
nonzero components per column. If k < n, this is usually not the
same solution as pinv(A)*B, which is the least squares solution with
the smallest norm
Thanks in advance for any help,
Giant.
p.s. If you can think of a better way of doing SVD with C, don't hesitate to tell me , I have run into a total brick wall here....