CMPS 290C Project Efficient Belief Propagation for Image Restoration
نویسنده
چکیده
The Markov Random Field (MRF) theory provides a consistent way for modeling context dependent entities such as image pixels. Trying to solve the image restoration problem in the MRF framework is an optimization problem that is NP hard, and approximation techniques like the belief propagation methods are proposed. The problem of the belief propagation is its inefficiency. In this project, I implement the efficient belief propagation method proposed by Felzenszwalb and Huttenlocher, applying it to additive noise removal and image inpainting. Further, other methods for additive noise removal like the total variation based, the bilateral based and the mean shift based methods are studied and compared with the efficient belief propagation based one. This project is part of the course requirements for CMPS 290Probablistic Graphical Model. Keywords—Efficient belief propagation, Image restoration, Markov Random field modeling —————————— ——————————
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