Paper Info
Title
Super Resolution for Remote Sensing Images Based on a Universal Hidden Markov Tree Model
Abstract
In this paper, we propose a new super resolution (SR) method called the maximum a posteriori based on a universal Hidden Markov Tree (HMT) model for remote sensing images. The HMT theory is first used to set up a prior model for reconstructing super resolved images from a sequence of warped, blurred, subsampled, and noise-contaminated low-resolution (LR) images. Because the wavelet coefficients of...
Year
DOI
Venue
2010
10.1109/TGRS.2009.2031636
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Hidden Markov models,Image resolution,Remote sensing,Strontium,Wavelet coefficients,Testing,Satellites,Image reconstruction,Gaussian distribution,Layout
Iterative reconstruction,Computer vision,Panchromatic film,Remote sensing,Artificial intelligence,Discrete wavelet transform,Maximum a posteriori estimation,Hidden Markov model,Image resolution,Mathematics,Wavelet,Wavelet transform
Journal
Volume
Issue
ISSN
48
3
0196-2892
Citations 
PageRank 
References 
25
0.78
16
Authors
4
Name
Order
Citations
PageRank
Feng Li1636.95
Xiuping Jia21424126.54
Donald Fraser3788.29
Andrew Lambert4272.28