A review of graph neural networks and their applications in power systems
W Liao, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2021 - ieeexplore.ieee.org
Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …
ranging from pattern recognition to signal processing. The data in these tasks are typically …
[PDF][PDF] A comprehensive review of deep learning-based single image super-resolution
SMA Bashir, Y Wang, M Khan, Y Niu - PeerJ Computer Science, 2021 - peerj.com
Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …
resolution of an image in the field of computer vision. In the last two decades, significant …
Srformer: Permuted self-attention for single image super-resolution
Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …
super-resolution models (eg, SwinIR) can significantly improve the model performance but …
Single image super-resolution based on directional variance attention network
Recent advances in single image super-resolution (SISR) explore the power of deep
convolutional neural networks (CNNs) to achieve better performance. However, most of the …
convolutional neural networks (CNNs) to achieve better performance. However, most of the …
Hitchhiker's guide to super-resolution: Introduction and recent advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving
research area. However, despite promising results, the field still faces challenges that …
research area. However, despite promising results, the field still faces challenges that …
[HTML][HTML] Geoscience-aware deep learning: A new paradigm for remote sensing
Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …
Multi-level graph learning network for hyperspectral image classification
Abstract Graph Convolutional Network (GCN) has emerged as a new technique for
hyperspectral image (HSI) classification. However, in current GCN-based methods, the …
hyperspectral image (HSI) classification. However, in current GCN-based methods, the …
Recurrent wavelet structure-preserving residual network for single image deraining
The combination of deep learning and image prior has been widely used in single image
deraining since 2017. Recent studies have demonstrated an excellent deraining effect on …
deraining since 2017. Recent studies have demonstrated an excellent deraining effect on …
TGF: Multiscale transformer graph attention network for multi-sensor image fusion
Multisensor image fusion is a challenging task that aims to produce a composite image by
fusing visible (VI) and infrared (IR) images. Deep neural networks have shown impressive …
fusing visible (VI) and infrared (IR) images. Deep neural networks have shown impressive …
Comparison of DEM super-resolution methods based on interpolation and neural networks
High-resolution digital elevation models (DEMs) play a critical role in geospatial databases,
which can be applied to many terrain-related studies such as facility siting, hydrological …
which can be applied to many terrain-related studies such as facility siting, hydrological …