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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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: 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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Book ChapterDOI
Example-Based Learning for Single-Image Super-Resolution
Kwang In Kim,Younghee Kwon +1 more
TL;DR: In this article, a regression-based method for single image super-resolution is proposed, which combines the ideas of kernel matching pursuit and gradient descent, which allows time complexity to be kept to a moderate level.
Proceedings ArticleDOI
Image super resolution-A survey
TL;DR: There are various techniques to attain an image with higher resolution and in this paper some of the approaches of super resolution are discussed.
Proceedings ArticleDOI
Hallucinating faces: TensorPatch super-resolution and coupled residue compensation
Wei Liu,Dahua Lin,Xiaoou Tang +2 more
TL;DR: A new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models, and develops an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space.
Journal ArticleDOI
Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution
TL;DR: This paper believes that textures may be contained in multiple manifolds, corresponding to classes, and presents a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE).
Journal ArticleDOI
Super-Resolution Image Restoration from Blurred Low-Resolution Images
Michael K. Ng,Andy C. Yau +1 more
TL;DR: It is shown that with the periodic boundary condition, the high-resolution image can be restored efficiently by using fast Fourier transforms and the preconditioned conjugate gradient method is applied.