35 citations · 88 across the 9 of their papers we have counts for
9 papers
Interpretable Deep Multimodal Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality. While existing de…
Multimodal Deep Unfolding for Guided Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
The reconstruction of a high resolution image given a low resolution observation is an ill-posed inverse problem in imaging. Deep learning methods rely on training data to learn an…
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge abo…
Twitter User Geolocation using Deep Multiview Learning
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows…
Multiview Deep Learning for Predicting Twitter Users' Location
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni +2
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing.…
Multi-modal dictionary learning for image separation with application in art investigation
Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis +2
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals…
X-ray image separation via coupled dictionary learning
Nikos Deligiannis, João F. C. Mota, Bruno Cornelis +2
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation m…
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
L. Jacques, C. De Vleeschouwer, Y. Boursier +64
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…
Bayesian crack detection in ultra high resolution multimodal images of paintings
Bruno Cornelis, Yun Yang, Joshua T. Vogelstein +3
The preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are develo…