TY - GEN
T1 - Bayesian image separation with natural image prior
AU - Zhang, Haichao
AU - Zhang, Yanning
PY - 2012
Y1 - 2012
N2 - Image separation from a set of observed mixtures has important applications in many fields such as intrinsic image extraction. We investigate in this work a natural image prior based image separation algorithm. The natural image prior is modeled via a high-order Markov Random Field (MRF) and is integrated into a Bayesian framework for estimating all the component images. Due to the usage of the natural image prior, which typically leading to non-convex optimization problems, there is no closed form solution for estimating the component images. Therefore, a Markov chain Monte-Carlo based sampling algorithm is developed for solution. Based on this, a Minimum Mean Square Error (MMSE) estimation can be achieved. The proposed method exploits both the mixing observations and the prior distribution of natural images, modeled via an MRF model. Experimental results indicate that the proposed method can generate better results than state-of-the-art image separation algorithms.
AB - Image separation from a set of observed mixtures has important applications in many fields such as intrinsic image extraction. We investigate in this work a natural image prior based image separation algorithm. The natural image prior is modeled via a high-order Markov Random Field (MRF) and is integrated into a Bayesian framework for estimating all the component images. Due to the usage of the natural image prior, which typically leading to non-convex optimization problems, there is no closed form solution for estimating the component images. Therefore, a Markov chain Monte-Carlo based sampling algorithm is developed for solution. Based on this, a Minimum Mean Square Error (MMSE) estimation can be achieved. The proposed method exploits both the mixing observations and the prior distribution of natural images, modeled via an MRF model. Experimental results indicate that the proposed method can generate better results than state-of-the-art image separation algorithms.
KW - Bayesian estimation
KW - Image separation
KW - natural image statistics
UR - http://www.scopus.com/inward/record.url?scp=84875856154&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6467305
DO - 10.1109/ICIP.2012.6467305
M3 - 会议稿件
AN - SCOPUS:84875856154
SN - 9781467325332
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2097
EP - 2100
BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012
Y2 - 30 September 2012 through 3 October 2012
ER -