Exponential Signal Reconstruction With Deep Hankel Matrix Factorization

• 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. IEEE

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Damping

Data acquisition

Deep learning (DL)

exponential signal

Hankel matrix

Image reconstruction

low rank

Matrix converters

Neural networks

Nuclear magnetic resonance

reconstruction.

Time-domain analysis

Exponential functions

Factorization

Iterative methods

Magnetic resonance

Matrix algebra

Medical imaging

Time domain analysis

Deep learning

Exponential signals

Exponentials

Images reconstruction

Matrix factorizations

Neural-networks

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)