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  Image denoising: Can plain Neural Networks compete with BM3D?

Burger, H., Schuler, C., & Harmeling, S. (2012). Image denoising: Can plain Neural Networks compete with BM3D? In 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012) (pp. 2392-2399). Piscataway, NJ, USA: IEEE.

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 Creators:
Burger, HC1, Author           
Schuler, CJ1, Author           
Harmeling, S1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches. While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.

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 Dates: 2012-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISBN: 978-1-4673-1226-4
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=arnumber=6247952
DOI: 10.1109/CVPR.2012.6247952
BibTex Citekey: BurgerSH2012
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Title: 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)
Place of Event: Providence, RI, USA
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Title: 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2392 - 2399 Identifier: -