A two-stage convolutional neural network for joint demosaicking and super-resolution
K Chang, H Li, Y Tan, PLK Ding… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
As two practical and important image processing tasks, color demosaicking (CDM) and
super-resolution (SR) have been studied for decades. However, most literature studies …
super-resolution (SR) have been studied for decades. However, most literature studies …
Non-linear perceptual multi-scale network for single image super-resolution
Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and achieved remarkable progress. However, most of the …
image super-resolution (SISR) and achieved remarkable progress. However, most of the …
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 …
MRDN: A lightweight multi-stage residual distillation network for image super-resolution
X Yang, Y Guo, Z Li, D Zhou, T Li - Expert Systems with Applications, 2022 - Elsevier
For the deep learning based super-resolution (SR) reconstruction method, researchers try to
expand the receptive field to improve the reconstruction quality. With the increase of network …
expand the receptive field to improve the reconstruction quality. With the increase of network …
CNN-based hyperspectral pansharpening with arbitrary resolution
Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its
panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the …
panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the …
TSRGAN: Real-world text image super-resolution based on adversarial learning and triplet attention
C Fang, Y Zhu, L Liao, X Ling - Neurocomputing, 2021 - Elsevier
The text in a low-resolution (LR) image is usually hard to read. Super-resolution (SR) is an
intuitive solution to this issue. Existing single image super-resolution (SISR) models are …
intuitive solution to this issue. Existing single image super-resolution (SISR) models are …
Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images
D Qiu, Y Cheng, X Wang, X Zhang - Computer Methods and Programs in …, 2021 - Elsevier
Background and objective With the increasing problem of coronavirus disease 2019 (COVID-
19) in the world, improving the image resolution of COVID-19 computed tomography (CT) …
19) in the world, improving the image resolution of COVID-19 computed tomography (CT) …
HASIC-Net: Hybrid attentional convolutional neural network with structure information consistency for spectral super-resolution of RGB images
Spectral super-resolution (SSR), referring to the recovery of a reasonable hyperspectral
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
Toward pixel-level precision for binary super-resolution with mixed binary representation
Binary neural network (BNN) is an effective method for reducing model computational and
memory cost, which has achieved much progress in the super-resolution (SR) field …
memory cost, which has achieved much progress in the super-resolution (SR) field …
Feature balance for fine-grained object classification in aerial images
W Zhao, T Tong, L Yao, Y Liu, C Xu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Fine-grained object classification (FGOC) focuses on identifying subcategories of objects,
which is crucial in military and civilian. Existing FGOC methods primarily focus on high …
which is crucial in military and civilian. Existing FGOC methods primarily focus on high …