42 citations · 144 across the 11 of their papers we have counts for
12 papers
Exploring Deep Learning Image Super-Resolution for Iris Recognition
Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez +1
In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here,…
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
Tomasz Tarasiewicz, Jakub Nalepa, Reuben A. Farrugia +4
Multispectral Sentinel-2 images are a valuable source of Earth observation data, however spatial resolution of their spectral bands limited to 10 m, 20 m, and 60 m ground sampling…
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
Matthew Aquilina, Keith George Ciantar, Christian Galea +3
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-reso…
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
Fernando Alonso-Fernandez, Reuben A. Farrugia, Josef Bigun
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a…
Face2Text revisited: Improved data set and baseline results
Marc Tanti, Shaun Abdilla, Adrian Muscat +3
Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we develope…
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
F. Alonso-Fernandez, R. A. Farrugia, J. Bigun +2
The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution…
Improving Super-Resolution Performance using Meta-Attention Layers
Matthew Aquilina, Christian Galea, John Abela +2
Convolutional Neural Networks (CNNs) have achieved impressive results across many super-resolution (SR) and image restoration tasks. While many such networks can upscale low-resolu…
Automated segmentation of microtomography imaging of Egyptian mummies
Marc Tanti, Camille Berruyer, Paul Tafforeau +6
Propagation Phase Contrast Synchrotron Microtomography (PPC-SRCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remai…
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Reuben A. Farrugia, C. Guillemot
Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved…
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Albert Gatt, Marc Tanti, Adrian Muscat +6
The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating te…
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Reuben A. Farrugia, Christine Guillemot
Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field…
Face Hallucination using Linear Models of Coupled Sparse Support
Reuben Farrugia, Christine Guillemot
Most face super-resolution methods assume that low-resolution and high-resolution manifolds have similar local geometrical structure, hence learn local models on the lowresolution…