Title :
An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals
Author :
Wohlberg, Brendt ; Rodríguez, Paul
Author_Institution :
Los Alamos Nat. Lab., Los Alamos
Abstract :
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard lscr2data fidelity term with an lscr1 norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the lscr2 -TV and lscr1 -TV problems as special cases. This method offers competitive computational performance for lscr2 -TV and is comparable to or faster than any other lscr1 -TV algorithms of which we are aware.
Keywords :
deconvolution; image denoising; image restoration; inverse problems; image deconvolution; image denoising; image restoration problem; inverse problem; iteratively reweighted norm algorithm; lscr1 norm; lscr2data fidelity term; total variation functional minimization; Deconvolution; Forward contracts; Image restoration; Inverse problems; Iterative algorithms; Minimization methods; Noise reduction; Sparse matrices; TV; Vectors; Image restoration; inverse problem; regularization; total variation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2007.906221
