TY - JOUR
T1 - A higher-order MRF based variational model for multiplicative noise reduction
AU - Chen, Yunjin
AU - Feng, Wensen
AU - Ranftl, Rene
AU - Qiao, Hong
AU - Pock, Thomas
PY - 2014
Y1 - 2014
N2 - The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Motivated by the successes of FoE-based approaches, in this letter we propose a novel variational model for multiplicative noise reduction based on the FoE image prior model. The resulting model corresponds to a non-convex minimization problem, which can be efficiently solved by a recently published non-convex optimization algorithm. Experimental results based on synthetic speckle noise and real synthetic aperture radar (SAR) images suggest that the performance of our proposed method is on par with the best published despeckling algorithm. Besides, our proposed model comes along with an additional advantage, that the inference is extremely efficient. Our GPU based implementation takes less than 1s to produce state-of-the-art despeckling performance.
AB - The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Motivated by the successes of FoE-based approaches, in this letter we propose a novel variational model for multiplicative noise reduction based on the FoE image prior model. The resulting model corresponds to a non-convex minimization problem, which can be efficiently solved by a recently published non-convex optimization algorithm. Experimental results based on synthetic speckle noise and real synthetic aperture radar (SAR) images suggest that the performance of our proposed method is on par with the best published despeckling algorithm. Besides, our proposed model comes along with an additional advantage, that the inference is extremely efficient. Our GPU based implementation takes less than 1s to produce state-of-the-art despeckling performance.
KW - Despeckling
KW - speckle noise
KW - Fields of Experts
KW - MRFs
KW - non-convex optimization
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6851186
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6851186
U2 - 10.1109/LSP.2014.2337274
DO - 10.1109/LSP.2014.2337274
M3 - Article
VL - 21
SP - 1370
EP - 1374
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
SN - 1070-9908
IS - 11
ER -