A general formulation of constrained iterative restoration algorithms
Aggelos K. Katsaggelos, Jan Biemond, R. Mersereau, Ronald W. Schafer · 2005
This paper introduces a general formulation of constrained iterative restoration algorithms in which deterministic and/or statistical information about the undistorted signal and statistical information about the noise are directly incorporated into the iterative procedure. This a priori information is incorporated into the restoration algorithm by what we call "soft" or statistical constraints. Their effect on the solution depends on the amount of noise on the data; that is, the constraint operator is "turned off" for noiseless data. The development of the new iterative algorithm is based on results from regularization techniques for stabilizing ill-posed problems.