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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Journal ArticleDOI
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
Tomer Peleg,Michael Elad +1 more
TL;DR: The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
Proceedings ArticleDOI
Image sequence enhancement using sub-pixel displacements
TL;DR: The subpixel registration allows image enhancement with respect to improved resolution and noise cleaning and is particularly useful for image sequences taken from an aircraft or satellite where images in a sequence differ mostly by translation and rotation.
Proceedings ArticleDOI
High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration
TL;DR: A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-resolution image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred images.
Journal ArticleDOI
High-Resolution Image Reconstruction from a Sequence of Rotated and Translated Frames and its Application to an Infrared Imaging System
TL;DR: A technique for estimating a high resolution image, with reduced aliasing, from a sequence of undersampled rotated and translationally shifted frames and shows that with the proper choice of a tuning parameter, the algorithm exhibits robustness in the presence of noise.
Journal ArticleDOI
On Bayesian Adaptive Video Super Resolution
Ce Liu,Deqing Sun +1 more
TL;DR: This paper proposes a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel, and noise level while reconstructing the original high-resolution frames and confirms empirical observations that an intermediate size blur kernel achieves the optimal image reconstruction results.