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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Book ChapterDOI
Remote Sensing Image Super-Resolution Using Residual Dense Network
TL;DR: This work implements image super-resolution for satellite images using a residual dense network (RDN), a CNN-based model that utilizes the hierarchic features from the input low resolution (LR) images and combines both the specific and general features present in the image, resulting in a better performance.
Proceedings ArticleDOI
A Review of Super Resolution Based on Deep Learning
TL;DR: In this article , the authors integrate and analyze the existing deep learning based image super-resolution models, and show several models with the best performance, and divide the models into five main categories based on where the sampling is located in the different models.
Proceedings ArticleDOI
Methods of Weighted Combination for Text Field Recognition in a Video Stream.
TL;DR: In this paper, a weighted text string recognition results combination method and weighting criteria was proposed to improve the quality of the video stream recognition result, based on the obtained results, it was concluded that the use of such weighted combination is appropriate for improving the quality.
Proceedings ArticleDOI
Variational Bayesian super resolution acceleration using preconditioned conjugate gradient
Jingyu Chen,Yigang Wang,Shi Li +2 more
TL;DR: Preconditioned Conjugate Gradient (PCG) is proposed to solve the problem of variational Bayesian SR algorithms and analyze the performance of the different PCG solvers, Jacobi and incomplete Cholesky decomposition (IC).
Book ChapterDOI
Dual-Convolutional Enhanced Residual Network for Single Super-Resolution of Remote Sensing Images
TL;DR: Dual-Convolutional Enhanced Residual Network (DCER) is proposed for remote sensing images based on residual learning, which concatenates the feature maps of different convolutional kernel sizes and can learn more high-frequency detail information by combining the local details of different scales.
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.