Journal ArticleDOI
Image super-resolution
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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
A Total Fractional-Order Variation Model for Image Super-Resolution and Its SAV Algorithm
TL;DR: A hybrid single-image super-resolution model integrated with total variation (TV) and fractional-order TV is proposed to provide an effective reconstruction of the HR image and an efficient numerical scheme using the scalar auxiliary variable approach with an adaptive time stepping strategy is developed.
Journal ArticleDOI
Data-adaptive low-rank modeling and external gradient prior for single image super-resolution
TL;DR: A data-adaptive low-rank (DLR) model is proposed that outperform many state-of-the-art single image SR methods in terms of both objective and subjective qualities.
Journal ArticleDOI
An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network
TL;DR: This work proposes an unsupervised SR method that does not require HR remote sensing images and introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images.
Journal ArticleDOI
Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review
Yoong Khang Ooi,Haidi Ibrahim +1 more
TL;DR: Image super-resolution convolutional neural network (SRCNN) was the pioneer of CNN-based algorithms, and it continued being improved till today through different techniques, including the type of loss functions used, upsampling module deployed, and the adopted network design strategies.
Journal ArticleDOI
A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry.
Klára Ščupáková,Klára Ščupáková,Vasilis Terzopoulos,Saurabh Jain,Dirk Smeets,Ron M. A. Heeren +5 more
TL;DR: The potential applicability of PBSR in a clinical setting is demonstrated by accurately integrating structural and molecular information from a case study of a dog liver and the performance of the PBSR was compared with other well-known methods.
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.