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Video super-resolution based on deep learning: a comprehensive survey
Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …
ones. Recently, the VSR methods based on deep neural networks have made great …
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 …
Cunerf: Cube-based neural radiance field for zero-shot medical image arbitrary-scale super resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution
Magnetic resonance images (MRI) acquired with low through-plane resolution compromise
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
Recent advancements and future prospects in active deep learning for medical image segmentation and classification
Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
Deep learning in medical image super resolution: a review
Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification
Abstract Acute Lymphoblastic Leukemia is one of the fatal types of disease which causes a
high mortality rate among children and adults. Traditional diagnosing of this disease is …
high mortality rate among children and adults. Traditional diagnosing of this disease is …
Ctspine1k: A large-scale dataset for spinal vertebrae segmentation in computed tomography
Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine
imaging is an essential tool for noninvasively visualizing and assessing spinal pathology …
imaging is an essential tool for noninvasively visualizing and assessing spinal pathology …
LIT-Former: Linking in-plane and through-plane transformers for simultaneous CT image denoising and deblurring
This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep
learning methods were developed in this context, typically they focus on 2D images and …
learning methods were developed in this context, typically they focus on 2D images and …
Expanding synthetic real-world degradations for blind video super resolution
Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have
drastically improved over the last few years and shown impressive performance on synthetic …
drastically improved over the last few years and shown impressive performance on synthetic …