Paper Info
Title
Wavelet Domain Deblurring and Denoising for Image Resolution Improvement
Abstract
In this paper, a new image interpolation method which is combined with deblurring and denoising is proposed. The MAP (Maximum a Posteriori) estimate is adopted to deal with the ill-conditioned problem (obtaining a super resolution image from a sub-sampled, blurred and contaminated image) in the wavelet domain. The universal hidden Markov tree (uHMT) theory in the wavelet domain is applied to construct a prior model for the MAP estimate. The results show that images reconstructed by our method are much better and sharper than those recovered images by the Huber- Markov random field (HMRF) prior model for MAP in the space domain.
Year
DOI
Venue
2007
10.1109/DICTA.2007.4426821
DICTA
Keywords
Field
DocType
universal hidden markov tree,image resolution improvement,prior model,markov random field,new image interpolation method,wavelet domain deblurring,wavelet domain,map estimate,ill-conditioned problem,super resolution image,space domain,contaminated image,noise reduction,super resolution,image resolution,interpolation,image reconstruction,hidden markov models,frequency,image processing,image interpolation,wavelet transforms
Computer vision,Deblurring,Pattern recognition,Markov random field,Computer science,Image processing,Artificial intelligence,Maximum a posteriori estimation,Image resolution,Image scaling,Wavelet transform,Wavelet
Conference
ISBN
Citations 
PageRank 
0-7695-3067-2
3
0.44
References 
Authors
6
3
Name
Order
Citations
PageRank
Feng Li1636.95
Donald Fraser2788.29
Xiuping Jia31424126.54