TY - JOUR
T1 - Deep Learning Denoising Based Line Spectral Estimation
AU - Jiang, Yuan
AU - Li, Hongbin
AU - Rangaswamy, Muralidhar
N1 - Publisher Copyright:
© 1994-2012 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - deep learning
KW - line spectral estimation
KW - signal denoising
UR - http://www.scopus.com/inward/record.url?scp=85077752796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077752796&partnerID=8YFLogxK
U2 - 10.1109/LSP.2019.2939049
DO - 10.1109/LSP.2019.2939049
M3 - Article
AN - SCOPUS:85077752796
SN - 1070-9908
VL - 26
SP - 1573
EP - 1577
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 11
M1 - 8822737
ER -