Search Results for author: Bruno Cornelis

Found 8 papers, 0 papers with code

Interpretable Deep Multimodal Image Super-Resolution

no code implementations7 Sep 2020 Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

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.

Image Super-Resolution

Multimodal Deep Unfolding for Guided Image Super-Resolution

no code implementations21 Jan 2020 Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution.

Image Super-Resolution Multimodal Deep Learning

Twitter User Geolocation using Deep Multiview Learning

no code implementations11 May 2018 Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far.

Multiview Learning

Multiview Deep Learning for Predicting Twitter Users' Location

no code implementations21 Dec 2017 Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features.

Multiview Learning

Multi-modal dictionary learning for image separation with application in art investigation

no code implementations14 Jul 2016 Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies

Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement.

Dictionary Learning

X-ray image separation via coupled dictionary learning

no code implementations20 May 2016 Nikos Deligiannis, João F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies

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.

Dictionary Learning

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