Title

Separable Markov Random Field Model And Its Applications In Low Level Vision

Keywords

Discriminative learning; image demosaicing; image denoising; Markov random field (MRF); separable filter

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. © 1992-2012 IEEE.

Publication Date

1-1-2013

Publication Title

IEEE Transactions on Image Processing

Volume

22

Issue

1

Number of Pages

402-408

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TIP.2012.2208981

Socpus ID

84871650887 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84871650887

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