Image super-resolution: A comprehensive review, recent trends, challenges and applications
Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …
processing to improve the visual perception of the poor-quality images. The key objective of …
Ntire 2017 challenge on single image super-resolution: Methods and results
This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …
details in an low resolution image) with focus on proposed solutions and results. A new …
Pulse: Self-supervised photo upsampling via latent space exploration of generative models
The primary aim of single-image super-resolution is to construct a high-resolution (HR)
image from a corresponding low-resolution (LR) input. In previous approaches, which have …
image from a corresponding low-resolution (LR) input. In previous approaches, which have …
Deep learning for image super-resolution: A survey
Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …
enhance the resolution of images and videos in computer vision. Recent years have …
[PDF][PDF] A comprehensive review of deep learning-based single image super-resolution
SMA Bashir, Y Wang, M Khan, Y Niu - PeerJ Computer Science, 2021 - peerj.com
Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …
resolution of an image in the field of computer vision. In the last two decades, significant …
Lightweight image super-resolution based on deep learning: State-of-the-art and future directions
Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …
more and more attention, aiming to improve the images and videos resolutions. Most of the …
Hitchhiker's guide to super-resolution: Introduction and recent advances
With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving
research area. However, despite promising results, the field still faces challenges that …
research area. However, despite promising results, the field still faces challenges that …
Single image super-resolution approaches in medical images based-deep learning: a survey
Abstract Medical image Super-Resolution (SR) reconstruction refers to the process of
regenerating a High-Resolution (HR) image from a degraded Low-Resolution (LR) image or …
regenerating a High-Resolution (HR) image from a degraded Low-Resolution (LR) image or …
[HTML][HTML] Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images
E Baccarelli, M Scarpiniti, A Momenzadeh - Expert Systems with …, 2023 - Elsevier
The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography
(CT) scans has become a very important task in modern medical diagnosis. Unfortunately …
(CT) scans has become a very important task in modern medical diagnosis. Unfortunately …
Spatial and channel aggregation network for lightweight image super-resolution
X Wu, L Zuo, F Huang - Sensors, 2023 - mdpi.com
Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are
designed to restore high-frequency image details and enhance imaging resolution through …
designed to restore high-frequency image details and enhance imaging resolution through …