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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 Pattern Recognition (pp. 206-215). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BA4C-A Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BA4D-8
Genre: Conference Paper

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 Creators:
Burger, HC1, Author           
Harmeling, S1, Author           
Mester M. Felsberg, R., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISBN: 978-3-642-23123-0
URI: http://www.springerlink.com/content/9jxp2u8p474661p7/fulltext.pdf
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
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Title: Pattern Recognition
Source Genre: Proceedings
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 206 - 215 Identifier: -