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
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Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution
Veronica Corona,Angelica I. Aviles-Rivero,Noémie Debroux,Carole Le Guyader,Carola-Bibiane Schönlieb +4 more
TL;DR: This work presents for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution, and demonstrates that this combination yields significant improvements over sequential models and other bi-task methods.
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Efficient $\ell^0$ gradient-based Super Resolution for simplified image segmentation
TL;DR: A variational model for single-image super-resolution based on the assumption that the gradient of the target image is sparse is considered, which can be used to improve the accuracy of standard segmentation algorithms for applications like QR codes and cell detection and land-cover classification problems.
Journal ArticleDOI
Polarimetric Imaging via Deep Learning: A Review
Xiaobo Liu,Lei Yan,Pengfei Qi,Liping Zhang,François Goudail,Tiegen Liu,Jingsheng Zhai,Hao-Chung Hu +7 more
TL;DR: Polarization can provide information largely uncorrelated with the spectrum and intensity, and therefore polarimetric imaging has significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles as discussed by the authors .
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Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments
Pasquale Cascarano,Maria Colomba Comes,Arianna Mencattini,Maria Carla Parrini,Elena Loli Piccolomini,Eugenio Martinelli +5 more
TL;DR: A new deep learning-based algorithm is presented that extends the well-known Deep Image Prior (DIP) to TLM Video Super Resolution without requiring any training and is validated on synthetic, i.e., artificially generated, as well as real videos from OOC experiments related to tumor-immune interaction.
Journal ArticleDOI
Video super-resolution with inverse recurrent net and hybrid local fusion
Dingyi Li,Zengfu Wang +1 more
TL;DR: Wang et al. as discussed by the authors proposed a hybrid local fusion method which uses parallel fusion and cascade fusion for incorporating sliding-window-based methods into their inverse recurrent net. But their method is not suitable for video super-resolution.
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.
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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.
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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.