TY - JOUR
T1 - Combining Poisson singular integral and total variation prior models in image restoration
AU - Ruiz, Pablo
AU - Madero-Orozco, Hiram
AU - Mateos, Javier
AU - Osiris Vergara-Villegas, Osslan
AU - Molina, Rafael
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
This research was supported by CONACYT , by the Spanish Ministry of Economy and Competitiveness under Project TIN2010-15137 , the European Regional Development Fund (FEDER), the CEI BioTic at the Universidad de Granada and, in part, by the US Department of Energy Grant DE-NA0000457 .
PY - 2014/10
Y1 - 2014/10
N2 - In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization-minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods.
AB - In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization-minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods.
KW - Bayesian image restoration
KW - Deblurring
KW - Denoising
KW - Poisson Singular Integral
KW - Total Variation
UR - http://www.scopus.com/inward/record.url?scp=84901200066&partnerID=8YFLogxK
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U2 - 10.1016/j.sigpro.2013.09.027
DO - 10.1016/j.sigpro.2013.09.027
M3 - Article
AN - SCOPUS:84901200066
VL - 103
SP - 296
EP - 308
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
ER -