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
Handling outliers in non-blind image deconvolution
TL;DR: A novel blur model is proposed that explicitly takes these outliers into account, and a robust non-blind deconvolution method is built upon it, which can effectively reduce the visual artifacts caused by outliers.
Journal ArticleDOI
A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video
TL;DR: This paper uses the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR.
Journal ArticleDOI
Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation
Leyuan Fang,Shutao Li,Ryan P. McNabb,Qing Nie,Anthony N. Kuo,Cynthia A. Toth,Joseph A. Izatt,Sina Farsiu +7 more
TL;DR: This novel image restoration method, which is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm, utilizes sparse representation dictionaries constructed from previously collected datasets and qualitatively and quantitatively outperforms other state-of-the-art methods.
Proceedings ArticleDOI
Total Variation-Based Image Deconvolution: a Majorization-Minimization Approach
TL;DR: A new TV-based algorithm for image deconvolution, under the assumptions of linear observations and additive white Gaussian noise is proposed, which has O(N) computational complexity, for finite support convolutional kernels.
Journal ArticleDOI
Iteratively Reweighted Least Squares
R. Wolke,H. Schwetlick +1 more
TL;DR: In this article, the authors proposed a new scale s as a minimization of the objective function g with respect to the residuals of the robust linear regression (RLR) problem.