A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Scientific Computing
A general heuristic for choosing the regularization parameter in ill-posed problems
SIAM Journal on Scientific Computing
Fast CG-Based Methods for Tikhonov--Phillips Regularization
SIAM Journal on Scientific Computing
Near-Optimal Parameters for Tikhonov and Other Regularization Methods
SIAM Journal on Scientific Computing
A Trust-Region Approach to the Regularization of Large-Scale Discrete Forms of Ill-Posed Problems
SIAM Journal on Scientific Computing
Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
SIAM Journal on Matrix Analysis and Applications
Hi-index | 0.09 |
Regularized approximations to the solutions of ill-posed problems typically vary from over-smoothed, inaccurate reconstructions to under-smoothed and unstable solutions as the regularization parameter varies about its optimal value. It thus makes sense to compare two (or more) regularized approximations, and seek the parametervalues where the distance between the approximations is a minimum. This paper advances the theory and practice of this methodology. The method appears to workvery well and be very stable, particularly in the presence of extreme error levels.