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
A reduced complexity approach to IAA beamforming for efficient DOA estimation of coherent sources
TL;DR: Two reduced-complexity (RC) versions of the IAA and IAA based on maximum likelihood (IAA-ML) algorithms are proposed and provide similar results to those obtained with their original counterparts.
Proceedings ArticleDOI
Penrose Pixels Super-Resolution in the Detector Layout Domain
TL;DR: A novel approach to reconstruction based super- resolution that explicitly models the detector's pixel layout and it is demonstrated that, in principle, this structure is better for super-resolution than the regular pixel array used in today's sensors.
Journal ArticleDOI
Spatially Adaptive Block-Based Super-Resolution
TL;DR: An adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which it incorporate reconstruction-based super- resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework.
Journal ArticleDOI
Accelerating 3B single-molecule super-resolution microscopy with cloud computing
TL;DR: The recently invented technique of Bayesian localization microscopy relaxes the requirement for an extremely high signal-to-background ratio for single-molecule super-resolution microscopy and reports here a simple solution for everyone: the Amazon Elastic Compute Cloud (Amazon EC2).
Proceedings ArticleDOI
Simultaneous super-resolution and 3D video using graph-cuts
TL;DR: A new method to increase the quality of 3D video, a new media developed to represent 3D objects in motion, by combining both super-resolution and dynamic 3D shape reconstruction problems into a unique Markov random field (MRF) energy formulation.