Enforcing nonnegativity in image reconstruction algorithms
J. Nagy and Z. Strakos
In image restoration and reconstruction applications, unconstrained
Krylov subspace methods represent an attractive approach for
computing approximate solutions. They
are fast, but unfortunately they do not produce approximate
solutions preserving nonnegativity. As a consequence
the error of the computed approximate solution can be large.
Enforcing a nonnegativity constraint can produce much more accurate
approximate solutions, but can also be computationally
This paper considers a nonnegatively constrained minimization
algorithm which represents a variant of an algorithm
proposed by Kaufman. Numerical experiments show that the
algorithm can be more accurate and computationally competitive
with unconstrained Krylov subspace methods.