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Deep Learning Denoising Based Line Spectral Estimation | IEEE Journals & Magazine | IEEE Xplore

Deep Learning Denoising Based Line Spectral Estimation


Abstract:

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising...Show More

Abstract:

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements. Following the denoising step, we employ a popular model order selection method and a subspace line spectral estimator to the denoised measurements for line spectral estimation. Numerical results show that the proposed approach outperforms a recently introduced atomic norm minimization based denoising method and offers a substantial improvement compared with the line spectral estimation results obtained by directly applying the subspace estimator without denoising.
Published in: IEEE Signal Processing Letters ( Volume: 26, Issue: 11, November 2019)
Page(s): 1573 - 1577
Date of Publication: 02 September 2019

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