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
Performance analysis on dictionary learning and sparse representation algorithms
Journal ArticleDOI
High-resolution two-photon fluorescence microscope imaging of nanodiamonds containing NV color centers
TL;DR: Two-photon fluorescence imaging of fluorescent nanodiamonds (FNDs) has been studied with a picosecond pulsed laser operating at 1032 nm in this paper .
Journal ArticleDOI
Very Small Image Face Recognition Using Deep Convolution Neural Networks
Julian Supardi,Shi-Jinn Horng +1 more
TL;DR: In this article, the authors proposed a novel method using deep convolution neural networks (DCNNs) for face image recognition with very small size, where first the image resolution is enhanced using CNN and then it is classified using the backpropagation algorithm.
Proceedings ArticleDOI
Multi-frame Super Resolution Reconstruction Based on Bayesian Inference
Xiaofeng Guo,Bo Qi,Jianliang Shi +2 more
Book ChapterDOI
Super-resolution-based GAN for image processing: Recent advances and future trends
TL;DR: To improve visual quality, the prime components of SRGAN—architecture, perceptual loss, adversarial loss, and training algorithm are studied and various potential open challenges, along with the future scope, are discussed.
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.