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Journal Publication

Multimodal Deep Unfolding for Guided Image Super-Resolution

This publication appears in: IEEE Transactions on Image Processing

Authors: I. Marivani, E. Tsiligianni, B. Cornelis and N. Deligiannis

Volume: 29

Pages: 8443-8456

Publication Date: Aug. 2020


Abstract:

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 high-resolution 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-of-the-art methods.

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Mr. Iman Marivani

+32 (0)02 629 167

imarivan@etrovub.be

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Dr. Ir. Bruno Cornelis

+32 (0)02 629 167

bcorneli@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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