MR image super-resolution with squeeze and excitation reasoning attention network
High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed
information for reliable diagnosis and quantitative image analyses. Deep convolutional …
information for reliable diagnosis and quantitative image analyses. Deep convolutional …
Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …
information important for clinical application and quantitative image analysis. However, HR …
MRI super-resolution with GAN and 3D multi-level DenseNet: smaller, faster, and better
High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical
information that is critical for diagnosis in the clinical application. However, HR MRI typically …
information that is critical for diagnosis in the clinical application. However, HR MRI typically …
MR image super-resolution via wide residual networks with fixed skip connection
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …
Sed: Semantic-aware discriminator for image super-resolution
Abstract Generative Adversarial Networks (GANs) have been widely used to recover vivid
textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to …
textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
Residual feature aggregation network for image super-resolution
Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …
single image super-resolution (SISR) and achieved significant improvements against …
Masa-sr: Matching acceleration and spatial adaptation for reference-based image super-resolution
Reference-based image super-resolution (RefSR) has shown promising success in
recovering high-frequency details by utilizing an external reference image (Ref). In this task …
recovering high-frequency details by utilizing an external reference image (Ref). In this task …
Efficient long-range attention network for image super-resolution
Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …
Ciaosr: Continuous implicit attention-in-attention network for arbitrary-scale image super-resolution
Learning continuous image representations is recently gaining popularity for image super-
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …