Title :
Separable Markov Random Field Model and Its Applications in Low Level Vision
Author :
Sun, Jian ; Tappen, Marshall F
Author_Institution :
Sch. of Math. & Stat., Xi´an Jiaotong Univ., Xi´an, China
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.
Keywords :
Markov processes; channel bank filters; computational complexity; computer vision; gradient methods; graphics processing units; image colour analysis; image denoising; image segmentation; learning (artificial intelligence); random processes; realistic images; 2D separable filter banks; 3D separable filter banks; MRF modeling; MRFSepa models; color image demosaicing; computational complexity; continuously-valued Markov random field model; gradient-based discriminative learning method; graphics processing unit; gray color image denoising; gray-scale image denoising; low level vision; potential functions; real-time image denoising; separable Markov random field model; Color; Computational modeling; Convolution; Graphics processing unit; Gray-scale; Noise reduction; Training; Discriminative learning; Markov random field (MRF); image demosaicing; image denoising; separable filter;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2012.2208981