Separable Markov Random Field Model and Its Applications in Low Level Vision
Abbreviated Journal Title
IEEE Trans. Image Process.
Discriminative learning; image demosaicing; image denoising; Markov; random field (MRF); separable filter; IMAGE; SPARSE; DOMAIN; FILTER; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic
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.
Ieee Transactions on Image Processing
"Separable Markov Random Field Model and Its Applications in Low Level Vision" (2013). Faculty Bibliography 2010s. 4725.