Noise Learning Based Denoising Autoencoder
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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 instead of the original 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 two case studies: symbol demodulation and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and less training dataset compared to DAE.
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