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
Discrete Total Variation with Finite Elements and Applications to Imaging
TL;DR: It can be shown that a variety of algorithms for classical image reconstruction problems, including TV-$$L^2$$L2 denoising and inpainting, can be implemented in low- and higher-order finite element spaces with the same efficiency as their counterparts originally developed for images on Cartesian grids.
Journal ArticleDOI
Global-local fusion network for face super-resolution
TL;DR: A novel global-local fused network (GLFSR) is designed to refine HF information for recovering fine details of facial images and demonstrates that GLFSR is superior to other state-of-the-art deep learning approaches.
Journal ArticleDOI
Deep learning methods in real-time image super-resolution: a survey
TL;DR: This paper provides a general overview on background technologies and pays special attention to super-resolution methods built on deep learning architectures for real-time super- resolution, which not only produce desirable reconstruction results, but also enlarge possible application scenarios of super resolution to systems like cell phones, drones, and embedding systems.
Posted Content
Learning When and Where to Zoom with Deep Reinforcement Learning
Burak Uzkent,Stefano Ermon +1 more
TL;DR: PatchDrop as discussed by the authors proposes a reinforcement learning approach to dynamically identify when and where to use/acquire high resolution data conditioned on the paired, cheap, low-resolution images, which achieves similar accuracy to models which use full high resolution images.
Journal ArticleDOI
Single image super-resolution using collaborative representation and non-local self-similarity
TL;DR: The proposed approach is capable of delivering higher quality of SR results than several state-of-the-art SR methods and an iterative algorithm is designed to gradually improve the quality of the SR results.
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.