Exponential Signal Reconstruction With Deep Hankel Matrix Factorization.
IEEE Trans Neural Netw Learn Syst
; 34(9): 6214-6226, 2023 Sep.
Article
em En
| MEDLINE
| ID: mdl-34941531
ABSTRACT
Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials' reconstruction method with the low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Ano de publicação:
2023
Tipo de documento:
Article