Separable Markov random field model and its applications in low level vision.
basic_science · Level V
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- Record sourced from PubMed, PMID 22829407.
- Also identified by DOI 10.1109/TIP.2012.2208981.
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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.