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  Improving Denoising Algorithms via a Multi-scale Meta-procedure

Burger, H., & Harmeling, S. (2011). Improving Denoising Algorithms via a Multi-scale Meta-procedure. In M. Mester, & R. Felsberg (Eds.), Pattern Recognition (pp. 206-215). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BA4C-A Version Permalink: http://hdl.handle.net/21.11116/0000-0001-F2DA-8
Genre: Conference Paper

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 Creators:
Burger, HC1, Author              
Harmeling, S1, Author              
Affiliations:
1Max Planck Institute for Intelligent Systems, Max Planck Society, Heisenbergstr. 3 70569 Stuttgart , DE, ou_1497638              

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 Abstract: Many state-of-the-art denoising algorithms focus on recovering high-frequency details in noisy images. However, images corrupted by large amounts of noise are also degraded in the lower frequencies. Thus properly handling all frequency bands allows us to better denoise in such regimes. To improve existing denoising algorithms we propose a meta-procedure that applies existing denoising algorithms across different scales and combines the resulting images into a single denoised image. With a comprehensive evaluation we show that the performance of many state-of-the-art denoising algorithms can be improved.

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 Dates: 2011-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-642-23123-0_21
BibTex Citekey: BurgerH2011
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Title: 33rd DAGM Symposium
Place of Event: Frankfurt a.M., Germany
Start-/End Date: -

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Title: Pattern Recognition
Source Genre: Proceedings
 Creator(s):
Mester, M, Editor
Felsberg, R, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 206 - 215 Identifier: ISBN: 978-3-642-23123-0