TY - GEN
T1 - An iteratively reweighted norm algorithm for total variation regularization
AU - Rodríguez, Paul
AU - Wohlberg, Brendt
PY - 2006
Y1 - 2006
N2 - Total Variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard ℓ2 data fidelity term with an ℓ1 norm. We propose a simple but very flexible and computationally efficient method, the Iteratively Reweighted Norm algorithm, for minimizing a generalized TV functional which includes both the ℓ2 -TV and and ℓ1-TV problems.
AB - Total Variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard ℓ2 data fidelity term with an ℓ1 norm. We propose a simple but very flexible and computationally efficient method, the Iteratively Reweighted Norm algorithm, for minimizing a generalized TV functional which includes both the ℓ2 -TV and and ℓ1-TV problems.
UR - http://www.scopus.com/inward/record.url?scp=47049089183&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2006.354879
DO - 10.1109/ACSSC.2006.354879
M3 - Conference contribution
AN - SCOPUS:47049089183
SN - 1424407850
SN - 9781424407859
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 892
EP - 896
BT - Conference Record of the 40th Asilomar Conference on Signals, Systems and Computers, ACSSC '06
T2 - 40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06
Y2 - 29 October 2006 through 1 November 2006
ER -