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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3014729
|2 doi
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Marivani, Iman
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Multimodal Deep Unfolding for Guided Image Super-Resolution
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a 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 end-to-end mapping from a low-resolution input to a highresolution output. Unlike existing deep multimodal models that do not incorporate domain knowledge about the problem, we propose a multimodal deep learning design that incorporates sparse priors and allows the effective integration of information from another image modality into the network architecture. Our solution relies on a novel deep unfolding operator, performing steps similar to an iterative algorithm for convolutional sparse coding with side information; therefore, the proposed neural network is interpretable by design. The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution. An alternative multimodal design is investigated by employing residual learning to improve the training efficiency. The presented multimodal approach is applied to super-resolution of near-infrared and multi-spectral images as well as depth upsampling using RGB images as side information. Experimental results show that our model outperforms state-ofthe-art methods
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| 650 |
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|a Journal Article
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1 |
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|a Tsiligianni, Evaggelia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cornelis, Bruno
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Deligiannis, Nikos
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2020) vom: 12. Aug.
|w (DE-627)NLM09821456X
|x 1941-0042
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|g year:2020
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|u http://dx.doi.org/10.1109/TIP.2020.3014729
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