Journal ArticleDOI
Image super-resolution
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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Proceedings ArticleDOI
High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur
TL;DR: This work develops a formulation that simultaneously takes into account blurring due to relative sensor-object motion, sensor integration, and additive noise, and proposes a POCS-based algorithm for performing the high-resolution reconstruction.
Journal ArticleDOI
Color to Gray: Visual Cue Preservation
TL;DR: A new algorithm based on a probabilistic graphical model with the assumption that the image is defined over a Markov random field is proposed and it is demonstrated that the proposed approach outperforms representative conventional algorithms in terms of effectiveness and efficiency.
Journal ArticleDOI
Super Resolution With Probabilistic Motion Estimation
Matan Protter,Michael Elad +1 more
TL;DR: This correspondence presents a new framework that ultimately leads to the same algorithm as in the prior work, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation.
Proceedings ArticleDOI
Geometry constrained sparse coding for single image super-resolution
TL;DR: A novel sparse coding method is proposed to preserve the geometrical structure of the dictionary and the sparse coefficients of the data and can preserve the incoherence of dictionary entries, which is critical for sparse representation.
Journal ArticleDOI
Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution
TL;DR: New methods for extracting features in low-resolution images in order to develop efficient registration techniques are proposed and the sampling theory of signals with finite rate of innovation is considered and some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration.