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Generalizing the non-local-means to super-resolution reconstruction (2009)
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| Venue: | IN IEEE TRANSACTIONS ON IMAGE PROCESSING |
| Citations: | 81 - 4 self |
BibTeX
@INPROCEEDINGS{Protter09generalizingthe,
author = {Matan Protter and Michael Elad and Hiroyuki Takeda and Peyman Milanfar},
title = {Generalizing the non-local-means to super-resolution reconstruction },
booktitle = {IN IEEE TRANSACTIONS ON IMAGE PROCESSING},
year = {2009},
pages = {36},
publisher = {}
}
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Abstract
Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.
Keyphrases
super-resolution reconstruction explicit motion estimation simple super-resolution algorithm state-of-the-art algorithm quality result accurate motion estimation fusion task several test movie general sequence classic super-resolution technique denoising method several low-quality image similar nature super-resolved outcome optical resolution general motion pattern processing sequence video denoising problem nonglobal motion field super-resolution algorithm recent development
