Perception consistency ultrasound image super-resolution via self-supervised CycleGAN
Due to the limitations of sensors, the transmission medium, and the intrinsic properties of
ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial …
ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial …
A fast medical image super resolution method based on deep learning network
S Zhang, G Liang, S Pan, L Zheng - IEEE Access, 2018 - ieeexplore.ieee.org
Low-resolution medical images can hamper medical diagnosis seriously, especially in the
analysis of retina images and specifically for the detection of macula fovea. Therefore …
analysis of retina images and specifically for the detection of macula fovea. Therefore …
R-FUSE: Robust fast fusion of multiband images based on solving a Sylvester equation
This letter proposes a robust fast multiband image fusion method to merge a high-spatial low-
spectral resolution image and a low-spatial high-spectral resolution image. Following the …
spectral resolution image and a low-spatial high-spectral resolution image. Following the …
Self super-resolution of optical coherence tomography images based on deep learning
Z Yuan, D Yang, W Wang, J Zhao, Y Liang - Optics Express, 2023 - opg.optica.org
As a medical imaging modality, many researches have been devoted to improving the
resolution of optical coherence tomography (OCT). We developed a deep-learning based …
resolution of optical coherence tomography (OCT). We developed a deep-learning based …
A multi-degradation aided method for unsupervised remote sensing image super resolution with convolution neural networks
N Zhang, Y Wang, X Zhang, D Xu… - … on Geoscience and …, 2020 - ieeexplore.ieee.org
In remote sensing, it is desirable to improve image resolution by using the image super-
resolution (SR) technique. However, there are two challenges: the first one is that high …
resolution (SR) technique. However, there are two challenges: the first one is that high …
Adaptive super-resolution for remote sensing images based on sparse representation with global joint dictionary model
Sparse representation has been widely used in the field of remote sensing image super-
resolution (SR) to restore a high-quality image from a low-resolution (LR) image, eg, from …
resolution (SR) to restore a high-quality image from a low-resolution (LR) image, eg, from …
Multispectral image super-resolution via RGB image fusion and radiometric calibration
Multispectral imaging is of wide application for its capability in acquiring the spectral
information of scenes. Due to hardware limitation, multispectral imaging device usually …
information of scenes. Due to hardware limitation, multispectral imaging device usually …
Unsupervised super-resolution framework for medical ultrasound images using dilated convolutional neural networks
J Lu, W Liu - 2018 IEEE 3rd International Conference on Image …, 2018 - ieeexplore.ieee.org
Ultrasound Imaging is one of the most widely used imaging modalities for clinic diagnosis,
but suffers from a low resolution due to the intrinsic physical flaws. In this paper, we present …
but suffers from a low resolution due to the intrinsic physical flaws. In this paper, we present …
Super resolution of B-mode ultrasound images with deep learning
H Temiz, HS Bilge - Ieee Access, 2020 - ieeexplore.ieee.org
Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its
natural intrinsic imaging characteristics, it produces poor quality images with low resolution …
natural intrinsic imaging characteristics, it produces poor quality images with low resolution …