Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Adversarial Denoising

Adversarial Learning for Denoising

Adversarial Learning for Denoising

Scientific Director: Vasileios Belagiannis

Contact Person(s): Leslie Casas


We present the idea of adversarial learning for one-dimensional signal denoising. An adversarial encoder-decoder network is proposed for denoising signals, represented by a sequence of measurements. We rely on the network’s latent representation as input to the discriminator to classify the signal into clean or noisy. Aligning the noisy and clean signal distributions results in removing the signal noise. Unlike the standard GAN training, we propose a new formulation that suits to one-dimensional signal denoising. In the evaluation, we show better performance than the related approaches, such as autoencoders and recurrent neural networks, demonstrating the benefits of our adversarial encoder-decoder network in different signal and noise types.


Figure 1: Noisy vs. Clean signal latent space - Before Denoising
Figure 2: Noisy vs. Clean signal latent space - After Denoising


Contact Person(s)

Leslie Casas
M.Sc. Leslie Casas


Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.

internal project page

Please contact Leslie Casas for available student projects within this research project.

Related Publications

L. Casas, N. Navab, V. Belagiannis
Adversarial Signal Denoising with Encoder-Decoder Networks
This works can be found at: (bib)

Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 30 Jan 2019 - 19:12 - LeslieCasas

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