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Noise Learning-Based Denoising Autoencoder
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DOI：10.1109/LCOMM.2021.3091800.png)
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Abstract
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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.
Keywords:
Noise reduction
Training
Noise measurement
Random variables
Encoding
Decoding
Internet of Things
Machine learning
noise learning based denoising autoencoder
signal restoration
symbol demodulation
precise localization
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4.4
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