Paper Info
Title
Universal HMT based super resolution for remote sensing images
Abstract
In this paper, we propose a new super resolution method Maximum a Posteriori based on a universal hidden Markov tree model (MAP-uHMT) for remote sensing images. The hidden Markov tree theory in the wavelet domain is used to set up a prior model for reconstructing super resolution images from a sequence of warped, blurred, sub-sampled and contaminated low resolution images. Both the simulation results with a Landsat7 panchromatic image and actual results with four Landsat7 panchromatic images which were captured on different dates show that our method achieves better super resolution images both visually and quantitatively than other methods, based on PSNR in the simulation and derived PSF with actual data.
Year
DOI
Venue
2008
10.1109/ICIP.2008.4711759
ICIP
Keywords
Field
DocType
remote sensing,sub-sampled image sequence,psnr,universal hmt,landsat7,wavelet transform,blurred image sequence,image resolution,universal hidden markov tree model,contaminated low resolution image sequence,super resolution image reconstruction,image reconstruction,landsat7 panchromatic image,image sequences,wavelet domain,super-resolution,warped image sequence,remote sensing images,hidden markov tree model,markov processes,maximum a posteriori,hidden markov models,indexing terms,super resolution,noise,strontium,wavelet transforms,low resolution
Markov process,Computer science,Remote sensing,Artificial intelligence,Wavelet transform,Wavelet,Iterative reconstruction,Computer vision,Pattern recognition,Panchromatic film,Maximum a posteriori estimation,Hidden Markov model,Image resolution
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-1764-3
978-1-4244-1764-3
21
PageRank 
References 
Authors
0.85
6
3
Name
Order
Citations
PageRank
Feng Li1636.95
Xiuping Jia21424126.54
Donald Fraser3788.29