Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30878
Title: Image magnification based on a blockwise adaptive Markov random field model
Authors: Zhang, X
Lam, KM 
Shen, L
Keywords: Image magnification
Iterated function systems
MAP estimation
Markov random field
Issue Date: 2008
Publisher: Elsevier Science Bv
Source: Image and vision computing, 2008, v. 26, no. 9, p. 1277-1284 How to cite?
Journal: Image and Vision Computing 
Abstract: In this paper, an effective image magnification algorithm based on an adaptive Markov random field (MRF) model with a Bayesian framework is proposed. A low-resolution (LR) image is first magnified to form a high-resolution (HR) image using a fractal-based method, namely the multiple partitioned iterated function system (MPIFS). The quality of this magnified HR image is then improved by means of a blockwise adaptive MRF model using the Bayesian 'maximum a posteriori' (MAP) approach. We propose an efficient parameter estimation method for the MRF model such that the staircase artifact will be reduced in the HR image. Experimental results show that, when compared to the conventional MRF model, which uses a fixed set of parameters for a whole image, our algorithm can provide a magnified image with the well-preserved edges and texture, and can achieve a better PSNR and visual quality.
URI: http://hdl.handle.net/10397/30878
DOI: 10.1016/j.imavis.2008.03.003
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

13
Last Week
0
Last month
0
Citations as of Oct 9, 2017

WEB OF SCIENCETM
Citations

10
Last Week
0
Last month
Citations as of Sep 21, 2017

Page view(s)

49
Last Week
8
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.