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Deep Convolutional Neural Network for Inverse Problems in Imaging
TLDR
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Expand
  • 875
  • 66
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Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting
  • K. Jin, J. C. Ye
  • Mathematics, Computer Science
  • IEEE Transactions on Image Processing
  • 17 June 2015
TLDR
In this paper, we propose a patch-based image inpainting method using a low-rank Hankel structured matrix completion approach. Expand
  • 101
  • 14
A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix
TLDR
An annihilating filter-based low-rank Hankel matrix approach is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Expand
  • 144
  • 10
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Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging
TLDR
We developed a deep learning-based diagnostic method for predicting cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). Expand
  • 72
  • 5
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Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal
  • K. Jin, J. C. Ye
  • Computer Science
  • IEEE Transactions on Image Processing
  • 1 March 2018
TLDR
We propose a novel impulse-noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to estimate any missing pixels in a corrupted image. Expand
  • 41
  • 5
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Convolutional Neural Networks for Inverse Problems in Imaging: A Review
TLDR
In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. Expand
  • 272
  • 3
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CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
TLDR
We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Expand
  • 121
  • 3
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Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation
TLDR
We propose a drastically different two-step Fourier compressive sampling framework in a continuous domain that can be implemented via measurement domain interpolation, after which signal reconstruction can be done using classical analytic reconstruction methods. Expand
  • 56
  • 3
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Time-Dependent Deep Image Prior for Dynamic MRI
TLDR
We propose a novel unsupervised deep-learning-based algorithm to solve the inverse problem found in dynamic magnetic resonance imaging (MRI). Expand
  • 14
  • 3
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Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography.
TLDR
In optical tomography, there exist certain spatial frequency components that cannot be measured due to the limited projection angles imposed by the numerical aperture of objective lenses. Expand
  • 142
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