Learning graph regularisation for guided super-resolution
We introduce a novel formulation for guided super-resolution. Its core is a differentiable
optimisation layer that operates on a learned affinity graph. The learned graph potentials …
optimisation layer that operates on a learned affinity graph. The learned graph potentials …
Rethinking multi-contrast mri super-resolution: Rectangle-window cross-attention transformer and arbitrary-scale upsampling
Recently, several methods have explored the potential of multi-contrast magnetic resonance
imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR …
imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR …
A hybrid network of cnn and transformer for lightweight image super-resolution
J Fang, H Lin, X Chen, K Zeng - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …
super-resolution. However, these existing architectures commonly build massive number of …
Self-calibrated efficient transformer for lightweight super-resolution
Recently, deep learning has been successfully applied to the single-image super-resolution
(SISR) with remarkable performance. However, most existing methods focus on building a …
(SISR) with remarkable performance. However, most existing methods focus on building a …
Transformer for single image super-resolution
Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …
deep learning. However, most existing studies focus on building more complex networks …
Dual learning-based graph neural network for remote sensing image super-resolution
Z Liu, R Feng, L Wang, W Han… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image
interpretation, and single image super-resolution (SISR) reconstruction technology is …
interpretation, and single image super-resolution (SISR) reconstruction technology is …
Multi-image super-resolution for remote sensing using deep recurrent networks
High-resolution satellite imagery is critical for various earth observation applications related
to environment monitoring, geoscience, forecasting, and land use analysis. However, the …
to environment monitoring, geoscience, forecasting, and land use analysis. However, the …
Swin2sr: Swinv2 transformer for compressed image super-resolution and restoration
Compression plays an important role on the efficient transmission and storage of images
and videos through band-limited systems such as streaming services, virtual reality or …
and videos through band-limited systems such as streaming services, virtual reality or …
Disentangling light fields for super-resolution and disparity estimation
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D
scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have …
scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have …