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Deep learning methods for solving linear inverse problems: Research directions and paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …
Innumerable attempts have been carried out to solve different variants of the linear inverse …
AI-based epileptic seizure detection and prediction in internet of healthcare things: a systematic review
Epilepsy is a neurological condition affecting around 50 million individuals worldwide,
reported by the World Health Organization. This is identified as a hypersensitive disease by …
reported by the World Health Organization. This is identified as a hypersensitive disease by …
Model-guided deep hyperspectral image super-resolution
The trade-off between spatial and spectral resolution is one of the fundamental issues in
hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution …
hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution …
Video anomaly detection with sparse coding inspired deep neural networks
This paper presents an anomaly detection method that is based on a sparse coding inspired
Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based …
Deep Neural Networks (DNN). Specifically, in light of the success of sparse coding based …
On the principles of parsimony and self-consistency for the emergence of intelligence
Ten years into the revival of deep networks and artificial intelligence, we propose a
theoretical framework that sheds light on understanding deep networks within a bigger …
theoretical framework that sheds light on understanding deep networks within a bigger …
A survey of deep nonnegative matrix factorization
WS Chen, Q Zeng, B Pan - Neurocomputing, 2022 - Elsevier
Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
Spectral mixture model inspired network architectures for hyperspectral unmixing
In many statistical hyperspectral unmixing approaches, the unmixing task is essentially an
optimization problem given a defined linear or nonlinear spectral mixture model. However …
optimization problem given a defined linear or nonlinear spectral mixture model. However …
MGDUN: an interpretable network for multi-contrast MRI image super-resolution reconstruction
Magnetic resonance imaging (MRI) Super-Resolution (SR) aims to obtain high resolution
(HR) images with more detailed information for precise diagnosis and quantitative image …
(HR) images with more detailed information for precise diagnosis and quantitative image …
Accurate and lightweight image super-resolution with model-guided deep unfolding network
Deep neural networks (DNNs) based methods have achieved great success in single image
super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …
super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …
Model-guided multi-contrast deep unfolding network for MRI super-resolution reconstruction
Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed
information for accurate diagnosis and quantitative image analysis. Despite the significant …
information for accurate diagnosis and quantitative image analysis. Despite the significant …