A Novel Super-Resolution Image Reconstruction Based on MRF

Author

Ma, YanJie ; Zhang, Hua ; Xue, Yanbing

Author_Institution

Tianjin Key Lab. of Intell. Comput. & Novel Software Technol., Tianjin Univ. of Technol., Tianjin, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

We address a novel method for super resolution based on Markov random field (MRF). Modeling image patches as MRF node, and we learn the parameters from training samples. Training sample set provide a candidate high-resolution interpretation for the low-resolution images. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-resolution patches for each patch of low-resolution image. In Bayesian belief propagation, we use compatibility relationships between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain the better result.

Keywords

Markov processes; belief networks; image reconstruction; image resolution; learning (artificial intelligence); Bayesian belief propagation; Markov random field; image patches; image resolution; sample set training; super-resolution image reconstruction; Bayesian methods; Belief propagation; Image reconstruction; Image resolution; Image storage; Laboratories; Markov random fields; Signal resolution; Strontium; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5305704

Filename

5305704