Noise Learning Based Denoising Autoencoder

This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE i...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Lee, Woong-Hee, Ozger, Mustafa, Challita, Ursula, Sung, Ki Won
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 21.06.2021
Subjects:	Demodulation Learning Noise reduction
ISSN:	2331-8422
Online Access:	Get full text
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Summary:	This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2101.07937