An l1-TV algorithm for deconvolution with salt and pepper noise
Conference
·
OSTI ID:960617
- Los Alamos National Laboratory
- NON LANL
There has recently been considerable interest in applying Total Variation with an {ell}{sup 1} data fidelity term to the denoising of images subject to salt and pepper noise, but the extension of this formulation to more general problems, such as deconvolution, has received little attention, most probably because most efficient algorithms for {ell}{sup 1}-TV denoising can not handle more general inverse problems. We apply the Iteratively Reweighted Norm algorithm to this problem, and compare performance with an alternative algorithm based on the Mumford-Shah functional.
- Research Organization:
- Los Alamos National Laboratory (LANL)
- Sponsoring Organization:
- DOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 960617
- Report Number(s):
- LA-UR-08-05496; LA-UR-08-5496
- Country of Publication:
- United States
- Language:
- English
Similar Records
A generalized vector-valued total variation algorithm
SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM
A faster-converging algorithm for image segmentation with a modified Chan-Vese model
Conference
·
Wed Dec 31 23:00:00 EST 2008
·
OSTI ID:956443
SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM
Technical Report
·
Sun Jan 07 23:00:00 EST 2007
·
OSTI ID:1000493
A faster-converging algorithm for image segmentation with a modified Chan-Vese model
Technical Report
·
Mon Nov 26 23:00:00 EST 2007
·
OSTI ID:1454974