Separable Markov Random Field Model and Its Applications in Low Level Vision

Abstract

This brief proposes a continuously-valued Markov random field (MRF) model with separable filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF modeling. In this framework, we design a novel gradient-based discriminative learning method to learn the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and fast image demosaicing with high-quality results.

DOI: 10.1109/TIP.2012.2208981

Extracted Key Phrases

10 Figures and Tables

Cite this paper

@article{Sun2013SeparableMR, title={Separable Markov Random Field Model and Its Applications in Low Level Vision}, author={Jian Sun and Marshall F. Tappen}, journal={IEEE Transactions on Image Processing}, year={2013}, volume={22}, pages={402-407} }