Noise Learning-Based Denoising Autoencoder

Woong Hee Lee, Mustafa Ozger, Ursula Challita, Ki Won Sung

Research output: Contribution to journalArticlepeer-review

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Abstract

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.

Original languageEnglish
Article number9462839
Pages (from-to)2983-2987
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Machine learning
  • Noise learning based denoising autoencoder
  • Precise localization
  • Signal restoration
  • Symbol demodulation

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