Song, P;
Rodrigues, MRD;
(2018)
Multi-modal Image Processing based on Coupled Dictionary Learning.
In:
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
IEEE
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Abstract
In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities. Since these multimodal images originate from the same phenomenon, it is realistic to assume that they share common attributes or characteristics. In this paper, we propose a multi-modal image processing framework based on coupled dictionary learning to capture similarities and disparities between different image modalities. In particular, our framework can capture favorable structure similarities across different image modalities such as edges, corners, and other elementary primitives in a learned sparse transform domain, instead of the original pixel domain, that can be used to improve a number of image processing tasks such as denoising, inpainting, or super-resolution. Practical experiments demonstrate that incorporating multimodal information using our framework brings notable benefits.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-modal Image Processing based on Coupled Dictionary Learning |
| Event: | 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 25-28 June 2018, Kalamata, Greece |
| ISBN-13: | 978-1-5386-3512-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SPAWC.2018.8446001 |
| Publisher version: | https://doi.org/10.1109/SPAWC.2018.8446001 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | multimodal image processing, coupled dictionary learning, joint sparse representation, denoising, inpainting, super-resolution |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10061952 |
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