A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Covariance-Preconditioned Iterative Methods for Nonnegatively Constrained Astronomical Imaging
SIAM Journal on Matrix Analysis and Applications
Original Article: Comparingparameter choice methods for regularization of ill-posed problems
Mathematics and Computers in Simulation
A practical stopping rule for iterative signal restoration
IEEE Transactions on Signal Processing
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We consider the two-dimensional discrete nonnegatively constrained deconvolution problem, whose goal is to reconstruct an object x^@? from its image b obtained through an optical system and affected by noise. When the large size of the problem prevents regularization through a direct method, iterative methods enjoying the semi-convergence property, coupled with suitable strategies for enforcing nonnegativity, are suggested. For these methods an accurate detection of the stopping index is essential. In this paper we analyze various stopping rules and, with the aid of a large experimentation, we test their effect on three different widely used iterative regularizing methods.