TY - JOUR
T1 - Alternating direction method of multipliers for nonlinear image restoration problems
AU - Chen, Chuan
AU - Ng, Michael K.
AU - Zhao, Xi Le
N1 - Funding information:
The work of M. K. Ng was supported in part by the Research Grants Council, General Research Fund, through the Hong Kong Baptist University (HKBU), Hong Kong, under Grant 202013, and in part by the HKBU Faculty Research Grant. The work of X.-L. Zhao was supported in part by 973 Program under Grant 2013CB329404, in part by the National Natural Science Foundation of China under Grant 61170311, Grant 61370147, Grant 61402082, in part by the Sichuan Province Science and Technology Research Project under Grant 2012GZX0080, and in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2013J106. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Javier Mateos.
Publisher copyright:
© 2014 IEEE.
PY - 2015/1
Y1 - 2015/1
N2 - In this paper, we address the total variation (TV)-based nonlinear image restoration problems. In nonlinear image restoration problems, an original image is corrupted by a spatially-invariant blur, the build-in nonlinearity in imaging system, and the additive Gaussian white noise. We study the objective function consisting of the nonlinear least squares data-fitting term and the TV regularization term of the restored image. By making use of the structure of the objective function, an efficient alternating direction method of multipliers can be developed for solving the proposed model. The convergence of the numerical scheme is also studied. Numerical examples, including nonlinear image restoration and high-dynamic range imaging are reported to demonstrate the effectiveness of the proposed model and the efficiency of the proposed numerical scheme.
AB - In this paper, we address the total variation (TV)-based nonlinear image restoration problems. In nonlinear image restoration problems, an original image is corrupted by a spatially-invariant blur, the build-in nonlinearity in imaging system, and the additive Gaussian white noise. We study the objective function consisting of the nonlinear least squares data-fitting term and the TV regularization term of the restored image. By making use of the structure of the objective function, an efficient alternating direction method of multipliers can be developed for solving the proposed model. The convergence of the numerical scheme is also studied. Numerical examples, including nonlinear image restoration and high-dynamic range imaging are reported to demonstrate the effectiveness of the proposed model and the efficiency of the proposed numerical scheme.
KW - Alternating direction method of multipliers
KW - High-dynamic range imaging
KW - Image restoration
KW - Nonlinearity
KW - Total variation
UR - http://www.scopus.com/inward/record.url?scp=84916928361&partnerID=8YFLogxK
U2 - 10.1109/TIP.2014.2369953
DO - 10.1109/TIP.2014.2369953
M3 - Journal article
AN - SCOPUS:84916928361
SN - 1057-7149
VL - 24
SP - 33
EP - 43
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 1
ER -