A new deep generative network for unsupervised remote sensing single-image super-resolution
JM Haut, R Fernandez-Beltran… - … on Geoscience and …, 2018 - ieeexplore.ieee.org
Super-resolution (SR) brings an excellent opportunity to improve a wide range of different
remote sensing applications. SR techniques are concerned about increasing the image …
remote sensing applications. SR techniques are concerned about increasing the image …
Remote sensing image super-resolution using novel dense-sampling networks
Super-resolution (SR) techniques play a crucial role in increasing the spatial resolution of
remote sensing data and overcoming the physical limitations of the spaceborne imaging …
remote sensing data and overcoming the physical limitations of the spaceborne imaging …
SWCGAN: Generative adversarial network combining swin transformer and CNN for remote sensing image super-resolution
Easy and efficient acquisition of high-resolution remote sensing images is of importance in
geographic information systems. Previously, deep neural networks composed of …
geographic information systems. Previously, deep neural networks composed of …
Scene-adaptive remote sensing image super-resolution using a multiscale attention network
Remote sensing image super-resolution has always been a major research focus, and many
deep-learning-based algorithms have been proposed in recent years. However, since the …
deep-learning-based algorithms have been proposed in recent years. However, since the …
Remote sensing single-image superresolution based on a deep compendium model
JM Haut, ME Paoletti… - … and Remote Sensing …, 2019 - ieeexplore.ieee.org
This letter introduces a novel remote sensing singleimage superresolution (SR) architecture
based on a deep efficient compendium model. The current deep learning-based SR trend …
based on a deep efficient compendium model. The current deep learning-based SR trend …
Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms
Y Li, S Mavromatis, F Zhang, Z Du… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
Super-resolution (SR) technology is an important way to improve spatial resolution under
the condition of sensor hardware limitations. With the development of deep learning (DL) …
the condition of sensor hardware limitations. With the development of deep learning (DL) …
Super-resolution of remote sensing images via a dense residual generative adversarial network
W Ma, Z Pan, F Yuan, B Lei - Remote Sensing, 2019 - mdpi.com
Single image super-resolution (SISR) has been widely studied in recent years as a crucial
technique for remote sensing applications. In this paper, a dense residual generative …
technique for remote sensing applications. In this paper, a dense residual generative …
Transformer-based multistage enhancement for remote sensing image super-resolution
Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …
Remote sensing image superresolution using deep residual channel attention
JM Haut, R Fernandez-Beltran… - … on Geoscience and …, 2019 - ieeexplore.ieee.org
The current trend in remote sensing image superresolution (SR) is to use supervised deep
learning models to effectively enhance the spatial resolution of airborne and satellite-based …
learning models to effectively enhance the spatial resolution of airborne and satellite-based …
Hybrid-scale self-similarity exploitation for remote sensing image super-resolution
Recently, deep convolutional neural networks (CNNs) have made great progress in remote
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …