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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Veröffentlicht in:IEEE communications letters Jg. 25; H. 9; S. 2983 - 2987
Hauptverfasser: Lee, Woong-Hee, Ozger, Mustafa, Challita, Ursula, Sung, Ki Won
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Decoding
Demodulation
Encoding
Internet of Things
Learning
Machine learning
noise learning based denoising autoencoder
Noise measurement
Noise reduction
precise localization
Random variables
signal restoration
symbol demodulation
Training
ISSN:1089-7798, 1558-2558, 1558-2558
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1089-7798
1558-2558
1558-2558
DOI:10.1109/LCOMM.2021.3091800