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
Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation
Gaoqi Liang,Gaoqi Liang,Guolong Liu,Junhua Zhao,Junhua Zhao,Yanli Liu,Jinjin Gu,Guangzhong Sun,Zhao Yang Dong +8 more
TL;DR: The problem of improving data completeness by recovering high- frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem and a novel machine-learning-based SRP approach is thereafter proposed.
Journal ArticleDOI
An iterative image super-resolution approach based on Bregman distance
TL;DR: A variational SR model based on Huber-Norm using Bregman distances is proposed, which is efficient in degraded image super-resolution task using primaldual algorithm and gives better performance comparing with other approaches.
Journal ArticleDOI
Robust Multi-image Processing with Optimal Sparse Regularization
TL;DR: This article reviews and extends results of the literature to the robustness to outliers of overdetermined signal recovery problems under sparse regularization, with a convex variational formulation, and shows that in the case of multi-image processing, the structure of the support of signal and noise must be studied precisely.
Journal ArticleDOI
Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation
Yongqin Zhang,Yongqin Zhang,Pew Thian Yap,Geng Chen,Weili Lin,Li Wang,Dinggang Shen,Dinggang Shen +7 more
TL;DR: This paper introduces a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites, and demonstrates that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.
Journal ArticleDOI
A convex variational method for super resolution of SAR image with speckle noise
TL;DR: A novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise is proposed that is one of the first works in this field and the split Bregman algorithm is employed efficiently.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.