The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

1 code implementation • Sensors 2023 • Matthew Aquilina, Keith George Ciantar, Christian Galea, Kenneth P. Camilleri, Reuben A. Farrugia, John Abela

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network.

Blind Super-Resolution Image Restoration +1

Improving Super-Resolution Performance using Meta-Attention Layers

1 code implementation • IEEE Signal Processing Letters 2021 • Matthew Aquilina, Christian Galea, John Abela, Kenneth P. Camilleri, Reuben A. Farrugia

While many such networks can upscale low-resolution (LR) images using just the raw pixel-level information, the ill-posed nature of SR can make it difficult to accurately super-resolve an image which has undergone multiple different degradations.

Image Restoration Image Super-Resolution

Word Probability Findings in the Voynich Manuscript

no code implementations • LREC 2020 • Colin Layfield, Lonneke van der Plas, Michael Rosner, John Abela

The Voynich Manuscript has baffled scholars for centuries.