Paper Info
Title
A Super Resolution Algorithm for Atmospherically Degraded Images Using Lucky Regions and MAP-uHMT
Abstract
This paper demonstrates the possibility of super resolved image reconstruction for images affected by atmospheric turbulence. A lucky region method using bicoherence is proposed to select image tiles with superior quality or “lucky image regions” from a large number of short exposure images. A super resolved image is then reconstructed by a MAP method based on a Universal Hidden Markov Tree model from the lucky regions. Performance is demonstrated with real data.
Year
DOI
Venue
2009
10.1109/DICTA.2009.94
DICTA
Keywords
Field
DocType
lucky region method,map method,short exposure image,universal hidden markov tree,image tile,lucky regions,large number,atmospherically degraded images,atmospheric turbulence,super resolution algorithm,image reconstruction,lucky image region,lucky region,atmospheric modeling,super resolution,imaging,hidden markov models,image restoration,bicoherence,mathematical model,nonlinear distortion,image resolution
Iterative reconstruction,Bicoherence,Computer vision,Super resolution algorithm,Pattern recognition,Computer science,Atmospheric model,Artificial intelligence,Image restoration,Hidden Markov model,Nonlinear distortion,Image resolution
Conference
Citations 
PageRank 
References 
1
0.48
7
Authors
5
Name
Order
Citations
PageRank
Zhi-ying Wen1659.91
Feng Li2636.95
Donald Fraser3788.29
Andrew Lambert4272.28
Xiuping Jia51424126.54