Noise Learning Based Denoising Autoencoder
This is an incremental improvement for denoising tasks in signal processing and related domains.
The paper tackles the problem of denoising by introducing a noise learning based denoising autoencoder (nlDAE) that learns and subtracts noise from input data, resulting in smaller latent space dimension and training dataset requirements compared to traditional DAE.
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.