TY - JOUR
T1 - Multiplicative noise removal via a learned dictionary
AU - Huang, Yu Mei
AU - Moisan, Lionel
AU - Ng, Kwok Po
AU - Zeng, Tieyong
N1 - Funding Information:
Manuscript received November 11, 2011; revised April 29, 2012; accepted May 18, 2012. Date of publication June 18, 2012; date of current version October 12, 2012. This work was supported in part by the NSFC Grant 11101195 and Grant 11171371, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20090211120011, and the China Post-Doctoral Science Foundation funded Project 2011M501488. The work of M. K. Ng was supported in part by the RGC grants and HKBU FRGs. The work of T. Zeng was supported by the RGC Grant 211710, Grant 211911, and Grant HKBU FRGs. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jose M. Bioucas-Dias.
PY - 2012/11
Y1 - 2012/11
N2 - Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.
AB - Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.
KW - Denoising
KW - dictionary
KW - multiplicative noise
KW - sparse representation
KW - variational model
UR - http://www.scopus.com/inward/record.url?scp=84867880275&partnerID=8YFLogxK
U2 - 10.1109/TIP.2012.2205007
DO - 10.1109/TIP.2012.2205007
M3 - Journal article
AN - SCOPUS:84867880275
SN - 1057-7149
VL - 21
SP - 4534
EP - 4543
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 11
M1 - 6220251
ER -