Separable Markov random field model and its applications in low level vision.
IEEE Transactions on Image Processing(2013)
摘要
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.
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关键词
separable filter,image denoising,discriminative learning,markov random field (mrf),image demosaicing,computer vision,computational complexity,random processes,markov processes,image segmentation,learning artificial intelligence
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