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
Maximum a posteriori blind image deconvolution with Huber-Markov random-field regularization.
Zhimin Xu,Edmund Y. Lam +1 more
TL;DR: Compared with the conventional maximum-likelihood method, the proposed maximum a posteriori blind deconvolution approach using a Huber-Markov random-field model not only suppresses noise effectively but also significantly alleviates the artifacts produced by the deconVolution process.
Journal ArticleDOI
A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors
Michael K. Ng,Andy M. Yip +1 more
TL;DR: This paper addresses how to use the Neumann boundary condition on the image, and the preconditionsed conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors.
Journal ArticleDOI
Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data
TL;DR: This issue is addressed here by means of differential tomography (Diff-Tomo), a recent multibaseline-multitemporal generalized interferometric framework which allows to resolve multiple moving scatterers at different heights in the same cell.
Journal ArticleDOI
Super-Resolution in Respiratory Synchronized Positron Emission Tomography
TL;DR: A maximum a posteriori (MAP) super-resolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation and lead to motion compensation combined with improved image SNR and contrast, without any increase in the overall acquisition times.
Proceedings ArticleDOI
Super-resolution mosaicking of UAV surveillance video
TL;DR: This paper explains and implements an efficient multi-frame super-resolution mosaicking algorithm that derives and implements a new hybrid regularization (bilateral total variance Hubert) method to solve the ill-posed large-scale inverse system.