A review of image super-resolution approaches based on deep learning and applications in remote sensing
At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …
images are becoming more widely used in real scenes. However, due to the limitations of …
Spherical pseudo-cylindrical representation for omnidirectional image super-resolution
Omnidirectional images have attracted significant attention in recent years due to the rapid
development of virtual reality technologies. Equirectangular projection (ERP), a naive form …
development of virtual reality technologies. Equirectangular projection (ERP), a naive form …
Multicontrast mri super-resolution via transformer-empowered multiscale contextual matching and aggregation
Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images
under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution …
under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution …
HIPA: hierarchical patch transformer for single image super resolution
Transformer-based architectures start to emerge in single image super resolution (SISR)
and have achieved promising performance. However, most existing vision Transformer …
and have achieved promising performance. However, most existing vision Transformer …
Bridging component learning with degradation modelling for blind image super-resolution
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited
impressive success on known degraded low-resolution (LR) images. However, this type of …
impressive success on known degraded low-resolution (LR) images. However, this type of …
MHGAN: A multi-headed generative adversarial network for underwater sonar image super-resolution
Z Ma, S Li, J Ding, B Zou - IEEE Transactions on Geoscience …, 2023 - ieeexplore.ieee.org
Super-resolution (SR) is a technique for recovering image details based on available
information, avoiding image quality degradation by increasing an image's resolution …
information, avoiding image quality degradation by increasing an image's resolution …
Single image super-resolution based on trainable feature matching attention network
Abstract Convolutional Neural Networks (CNNs) have been widely employed for image
Super-Resolution (SR) in recent years. Various techniques enhance SR performance by …
Super-Resolution (SR) in recent years. Various techniques enhance SR performance by …
Sea-net: Structure-enhanced attention network for limited-angle cbct reconstruction of clinical projection data
This work aims to improve limited-angle (LA) cone-beam computed tomography (CBCT) by
develo** deep learning (DL) methods for real clinical CBCT projection data, which is the …
develo** deep learning (DL) methods for real clinical CBCT projection data, which is the …
Perception-distortion balanced super-resolution: A multi-objective optimization perspective
High perceptual quality and low distortion degree are two important goals in image
restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to …
restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to …
A feature reuse framework with texture-adaptive aggregation for reference-based super-resolution
Reference-based super-resolution (RefSR) has gained considerable success in the field of
super-resolution with the addition of high-resolution reference images to reconstruct low …
super-resolution with the addition of high-resolution reference images to reconstruct low …