Abstract
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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
