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- W2971811912 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." @default.
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- W2971811912 date "2019-11-01" @default.
- W2971811912 modified "2023-10-03" @default.
- W2971811912 title "Deep Learning Denoising Based Line Spectral Estimation" @default.
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- W2971811912 doi "https://doi.org/10.1109/lsp.2019.2939049" @default.
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