Song, P;
Rodrigues, MRD;
(2018)
Multimodal Image Denoising based on Coupled Dictionary Learning.
In:
2018 25th IEEE International Conference on Image Processing (ICIP).
IEEE
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Abstract
In this paper, we propose a new multimodal image denoising approach to attenuate white Gaussian additive noise in a given image modality under the aid of a guidance image modality. The proposed coupled image denoising approach consists of two stages: coupled sparse coding and reconstruction. The first stage performs joint sparse transform for multimodal images with respect to a group of learned coupled dictionaries, followed by a shrinkage operation on the sparse representations. Then, in the second stage, the shrunken representations, together with coupled dictionaries, contribute to the reconstruction of the denoised image via an inverse transform. The proposed denoising scheme demonstrates the capability to capture both the common and distinct features of different data modalities. This capability makes our approach more robust to inconsistencies between the guidance and the target images, thereby overcoming drawbacks such as the texture copying artifacts. Experiments on real multimodal images demonstrate that the proposed approach is able to better employ guidance information to bring notable benefits in the image denoising task with respect to the state-of-the-art.
| Type: | Proceedings paper |
|---|---|
| Title: | Multimodal Image Denoising based on Coupled Dictionary Learning |
| Event: | 2018 25th IEEE International Conference on Image Processing (ICIP), 7-10 October 2018, Athens, Greece |
| ISBN-13: | 978-1-4799-7061-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICIP.2018.8451697 |
| Publisher version: | https://doi.org/10.1109/ICIP.2018.8451697 |
| 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 denosing, coupled dictionary learning, joint sparse representation, guidance information |
| 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.ucl.ac.uk/id/eprint/10061953 |
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