Search
Search Results
-
Image super resolution boosting using beta wavelet
Image super resolution (SR) is a critical category within the field of image processing techniques that aims to improve the resolution of both images...
-
Single image super-resolution: a comprehensive review and recent insight
Super-resolution (SR) is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the...
-
Deep learning-based magnetic resonance image super-resolution: a survey
Magnetic resonance imaging (MRI) is a medical imaging technique used to show anatomical structures and physiological processes of the human body. Due...
-
Infrared Image Super-Resolution via GAN
The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as...
-
CoT-MISR:Marrying convolution and transformer for multi-image super-resolution
Image super-resolution, a technique for image restoration, has been the subject of extensive research. The challenge lies in converting a...
-
Super-resolution reconstruction of single image for latent features
Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR)...
-
Superpixel Driven Unsupervised Deep Image Super-Resolution
Most of the existing deep learning-based image super-resolution methods require a large number of datasets or ground truth. However, these methods...
-
Multi-scale cross-fusion for arbitrary scale image super resolution
Deep convolutional neural networks (CNNs) have great improvements for single image super resolution (SISR). However, most of the existing SISR...
-
Image super-resolution reconstruction based on deep dictionary learning and A+
The method of image super-resolution reconstruction through the dictionary usually only uses a single-layer dictionary, which not only cannot extract...
-
Joint super-resolution and deblurring for low-resolution text image using two-branch neural network
The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical...
-
Enhanced collapsible linear blocks for arbitrary sized image super-resolution
Image up-scaling and super-resolution (SR) techniques have been a hot research topic for many years due to its large impact in the field of medical...
-
Deep primitive convolutional neural network for image super resolution
Deep networks have emerged as a dominant solution in many research areas recently. Numerous approaches based on deep networks have been developed for...
-
U-SRN: Convolutional Neural network for single image super resolution
Single Image Super Resolution (SISR) aims to recover high-frequency details of an image from its low-resolution form. It is a highly challenging...
-
HCT: a hybrid CNN and transformer network for hyperspectral image super-resolution
Recently, convolutional neural network (CNN) and transformer based on hyperspectral image super-resolution methods have achieved superior...
-
MadFormer: multi-attention-driven image super-resolution method based on Transformer
While the Transformer-based method has demonstrated exceptional performance in low-level visual processing tasks, it has a strong modeling ability...
-
Enhanced pyramidal residual networks for single image super-resolution
Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially,...
-
A novel spatial and spectral transformer network for hyperspectral image super-resolution
Recently, transformer networks based on hyperspectral image super-resolution have achieved significant performance in comparison with most...
-
Residual shuffle attention network for image super-resolution
The image super-resolution reconstruction methods based on deep learning achieve satisfactory visual quality; however, the majority are difficult to...
-
Spatial-Spectral Deep Residual Network for Hyperspectral Image Super-Resolution
Recently, single hyperspectral image super-resolution (SR) methods based on deep learning have been extensively studied. However, there has been...
-
Dtsr: detail-enhanced transformer for image super-resolution
Recently, Transformer has achieved remarkable success in image super-resolution (SR). However, most existing methods adopt the window-based...
