Journal ArticleDOI
Image super-resolution
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TLDR
This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.About:
This article is published in Signal Processing.The article was published on 2016-11-01. It has received 378 citations till now.read more
Citations
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An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
Journal ArticleDOI
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks.
Guotai Wang,Guotai Wang,Guotai Wang,Wenqi Li,Wenqi Li,Michael Aertsen,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Tom Vercauteren,Tom Vercauteren +10 more
TL;DR: In this article, a test-time augmentation-based aleatoric uncertainty was proposed to analyze the effect of different transformations of the input image on the segmentation output, and the results showed that the proposed test augmentation provides a better uncertainty estimation than calculating the testtime dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions.
Journal ArticleDOI
An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images
TL;DR: The proposed integrated fusion framework can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors.
Journal ArticleDOI
Super-Resolution for Remote Sensing Images via Local–Global Combined Network
Sen Lei,Zhenwei Shi,Zhengxia Zou +2 more
TL;DR: This letter proposes a new single-image super-resolution algorithm named local–global combined networks (LGCNet) for remote sensing images based on the deep CNNs, elaborately designed with its “multifork” structure to learn multilevel representations ofRemote sensing images including both local details and global environmental priors.
References
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Journal ArticleDOI
Adaptive total variation denoising based on difference curvature
TL;DR: Comparative results demonstrate that the new adaptive total variation method based on a new edge indicator, named difference curvature, can avoid the staircase effect and better preserve fine details.
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Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment
Xiao Xiang Zhu,Richard Bamler +1 more
TL;DR: The essential role of SR for layover separation in urban infrastructure monitoring is indicated by geometric and statistical analysis and it is shown that double scatterers with small elevation distances are more frequent than those with large elevation distances.
Journal ArticleDOI
A super-resolution reconstruction algorithm for hyperspectral images
TL;DR: A maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images and principal component analysis (PCA) is utilized in both parts of the proposed algorithm: motion estimation and image reconstruction.
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Novel Example-Based Method for Super-Resolution and Denoising of Medical Images
Dinh Hoan Trinh,Marie Luong,Françoise Dibos,Jean-Marie Rocchisani,Canh-Duong Pham,Truong Q. Nguyen +5 more
TL;DR: Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.
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Neighbor embedding based super-resolution algorithm through edge detection and feature selection
TL;DR: This work proposes an extended Neighbor embedding based super-resolution through edge detection and Feature Selection (henceforth NeedFS), which is robust even with a very limited training set and thus is promising for real applications.