Unsupervised learning method for general inverse problem and apparatus therefor
AssigneeKorea Advanced Institute of Science and Technology
InventorsJongChul Ye, Byeongsu Sim, Gyutaek Oh
Life of this patent
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Abstract
Disclosed are an unsupervised learning method and an apparatus therefor applicable to general inverse problems. An unsupervised learning method applicable to inverse problems includes receiving a training data set and training an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the receiving of the training data set includes receiving the training data set including unmatched data.
Claims9
An unsupervised learning method applicable to inverse problems, comprising:
- receiving a training data set;
- and
- training an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the neural network includes:
- a first neural network configured to convert a first magnetic resonance image (MRI) obtained, as an input, from an intermittent Fourier spatial coefficient into a second MRI corresponding to a complete Fourier spatial coefficient;
- a Fourier transform unit configured to output a third MRI corresponding to the first MRI by applying a Fourier transform and an inverse Fourier transform to the second MRI;
- and
- a second neural network configured to discriminate between the second MRI and an actual MRI for the second MRI.
An image processing method, comprising:
- receiving a first image;
- and
- reconstructing the first image as a second image corresponding to the first image using an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach, wherein the neural network includes:
- a first neural network configured to convert a first magnetic resonance image (MRI) obtained, as an input, from an intermittent Fourier spatial coefficient into a second MRI corresponding to a complete Fourier spatial coefficient;
- a Fourier transform unit configured to output a third MRI corresponding to the first MRI by applying a Fourier transform and an inverse Fourier transform to the second MRI;
- and
- a second neural network configured to discriminate between the second MRI and an actual MRI for the second MRI.
An unsupervised learning apparatus applicable to inverse problems, comprising:
- a receiving unit configured to receive a training data set;
- and
- a training unit configured to train an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the neural network includes:
- a first neural network configured to convert a first magnetic resonance image (MRI) obtained, as an input, from an intermittent Fourier spatial coefficient into a second MRI corresponding to a complete Fourier spatial coefficient;
- a Fourier transform unit configured to output a third MRI corresponding to the first MRI by applying a Fourier transform and an inverse Fourier transform to the second MRI;
- and
- a second neural network configured to discriminate between the second MRI and an actual MRI for the second MRI.
Dependencies are inferred from the claim text.