A review of the deep learning methods for medical images super resolution problems
Y Li, B Sixou, F Peyrin - Irbm, 2021 - Elsevier
Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …
medical images are not sufficient due to the constraints such as image acquisition time, low …
Medical image super-resolution reconstruction algorithms based on deep learning: A survey
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
Towards real-world blind face restoration with generative facial prior
Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
reference prior, to restore realistic and faithful details. However, very low-quality inputs …
A new generative adversarial network for medical images super resolution
For medical image analysis, there is always an immense need for rich details in an image.
Typically, the diagnosis will be served best if the fine details in the image are retained and …
Typically, the diagnosis will be served best if the fine details in the image are retained and …
Image super-resolution with an enhanced group convolutional neural network
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …
However, CNNs depend on deeper network architectures to improve performance of image …
A heterogeneous group CNN for image super-resolution
Convolutional neural networks (CNNs) have obtained remarkable performance via deep
architectures. However, these CNNs often achieve poor robustness for image super …
architectures. However, these CNNs often achieve poor robustness for image super …
Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution
Super-resolving medical images can help physicians in providing more accurate
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
diagnostics. In many situations, computed tomography (CT) or magnetic resonance imaging …
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 …
Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
[HTML][HTML] SOUP-GAN: Super-resolution MRI using generative adversarial networks
There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …
research applications. Image quality is inevitably traded off with acquisition time, which in …