[HTML][HTML] Medical image super-resolution for smart healthcare applications: A comprehensive survey
The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
Image super-resolution: The techniques, applications, and future
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …
the observed LR images. As SR has been developed for more than three decades, both …
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 …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
Accurate magnetic resonance image super-resolution using deep networks and Gaussian filtering in the stationary wavelet domain
G Suryanarayana, K Chandran, OI Khalaf… - IEEE …, 2021 - ieeexplore.ieee.org
In this correspondence, we present an accurate Magnetic Resonance (MR) image Super-
Resolution (SR) method that uses a Very Deep Residual network (VDR-net) in the training …
Resolution (SR) method that uses a Very Deep Residual network (VDR-net) in the training …
Spectral neural operators
VS Fanaskov, IV Oseledets - Doklady Mathematics, 2023 - Springer
In recent works, the authors introduced a neural operator: a special type of neural networks
that can approximate maps between infinite-dimensional spaces. Using numerical and …
that can approximate maps between infinite-dimensional spaces. Using numerical and …
Light field spatial super-resolution using deep efficient spatial-angular separable convolution
Light field (LF) photography is an emerging paradigm for capturing more immersive
representations of the real world. However, arising from the inherent tradeoff between the …
representations of the real world. However, arising from the inherent tradeoff between the …
Cardiac image super-resolution with global correspondence using multi-atlas patchmatch
The accurate measurement of 3D cardiac function is an important task in the analysis of
cardiac magnetic resonance (MR) images. However, short-axis image acquisitions with thick …
cardiac magnetic resonance (MR) images. However, short-axis image acquisitions with thick …
Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections
Image super-resolution technology successfully overcomes the limitation of excessively
large pixel size in infrared detectors and meets the increasing demand for high-resolution …
large pixel size in infrared detectors and meets the increasing demand for high-resolution …
Content-based processing and analysis of endoscopic images and videos: A survey
In recent years, digital endoscopy has established as key technology for medical screenings
and minimally invasive surgery. Since then, various research communities with manifold …
and minimally invasive surgery. Since then, various research communities with manifold …