< X, Y > = < [x1; x2; ...; xn], [y1; y2; ...; yn] >,
= [x1; x2; ...; xn]' * [y1; y2; ...; yn],
= SUM_(i=1)^(n) xi * yi,
= (x1 * y1) + (x2 * y2) + ... + (xn * yn).
(a + ib)' = (a - ib).
If A
satisfies the following relation,
< A * X, Y > = < X, AT * Y >,
then,
AT is transpose of A.
(1) 2D matrix
If A
is defined as follow,
A in R ^ (M, N),
then,
AT in R ^ (N, M).
If A(x)
is defined as follow,
A(x) = x(i+1) - x(i),
then AT(y)
is that,
AT(y) = y(i) - y(i+1).
If A(x)
is Fourier transform,
A(x) = fftn(x)/numel(x),
then AT(y)
is Inverse Fourier transform,
AT(y) = ifftn(y).
(4) Radon transform
If A(x)
is Radon transform called by 'Projection'
,
A(x) = radon(x, THETA)
where, THETA is degrees vector.
then AT(y)
is Inverse Radon transform without Filtration called by 'Backprojection'
,
AT(y) = iradon(y, THETA, 'none', N)/(pi/(2*length(THETA))).
where, 'none' is filtration option and N is image size.