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 generalized Gaussian image model for edge-preserving MAP estimation
TL;DR: In this article, a generalized Gaussian Markov random field (GGMRF) is proposed for image reconstruction in low-dosage transmission tomography, which satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data and invariance of the character of solutions to scaling of data.
Journal ArticleDOI
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Kwang In Kim,Younghee Kwon +1 more
TL;DR: Compared with existing algorithms, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images.
Proceedings ArticleDOI
Image super-resolution using gradient profile prior
TL;DR: An image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients is proposed.
Journal ArticleDOI
Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency
Michal Irani,Shmuel Peleg +1 more
TL;DR: Accurate computation of image motion enables the enhancement of image sequences that include improvement of image resolution, filling-in occluded regions, and reconstruction of transparent objects.