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Image denoising: Can plain Neural Networks compete with BM3D?

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83841

Burger,  HC
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84198

Schuler,  CJ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B72E-C
Zusammenfassung
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.