Pre-trained Pytorch Models used for MDPI Sensors paper titled 'The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks'

Name: Pre-trained Pytorch Models used for MDPI Sensors paper titled 'The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks'
Keywords: Deep Learning, Super-Resolution, Contrastive Learning, Convolutional Neural Networks, Blind Super-Resolution, Image Restoration, Meta-Attention, Iterative Degradation Prediction, Pytorch, Python

Aquilina, Matthew; Ciantar, Keith George; Galea, Christian; Camilleri, Kenneth; Farrugia, Reuben; Abela, John

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Software . 2022

License: https://www.gnu.org/licenses/gpl-3.0-standalone.html

Data sources: Datacite

ZENODO

Software . 2022

License: https://www.gnu.org/licenses/gpl-3.0-standalone.html

Data sources: Datacite

Pre-trained Pytorch Models used for MDPI Sensors paper titled 'The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks'

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 28 Dec 2022Publisher:Zenodo

Authors: Aquilina, Matthew; Ciantar, Keith George; Galea, Christian; Camilleri, Kenneth; Farrugia, Reuben; Abela, John;

doi: 10.5281/zenodo.7488458 , 10.5281/zenodo.7488457

Pre-trained Pytorch Models used for MDPI Sensors paper titled 'The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks'

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Abstract

This collection contains all the pre-trained model weights used to produce the results in our paper titled 'The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks' (link here). Instructions on how to use these models is provided in our Github repo here.

These models were produced as part of the Deep-FIR project, which is financed by the Malta Council for Science & Technology (MCST) (grant number R&I-2017-002-T), for and on behalf of the Foundation for Science & Technology, through the FUSION: R&I Technology Development Programme.

Related Organizations

University of Malta
Malta

Keywords

Deep Learning, Super-Resolution, Contrastive Learning, Convolutional Neural Networks, Blind Super-Resolution, Image Restoration, Meta-Attention, Iterative Degradation Prediction, Pytorch, Python

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