Title

Separable Markov Random Field Model and Its Applications in Low Level Vision

Authors

Authors

J. Sun;M. F. Tappen

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Image Process.

Keywords

Discriminative learning; image demosaicing; image denoising; Markov; random field (MRF); separable filter; IMAGE; SPARSE; DOMAIN; FILTER; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

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.

Journal Title

Ieee Transactions on Image Processing

Volume

22

Issue/Number

1

Publication Date

1-1-2013

Document Type

Article

DOI Link

http://dx.doi.org/10.1109/tip.2012.2208981

Language

English

First Page

402

Last Page

408

WOS Identifier

WOS:000312892000032

ISSN

1057-7149

Recommended Citation

"Separable Markov Random Field Model and Its Applications in Low Level Vision" (2013). Faculty Bibliography 2010s. 4725.
https://stars.library.ucf.edu/facultybib2010/4725