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Conference Publication

Joint Image Super-Resolution Via Recurrent Convolutional Neural Networks With Coupled Sparse Priors

Host Publication: IEEE International Conference on Image Processing

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

Publisher: IEEE

Publication Year: 2020

Number of Pages: 5


Abstract:

Joint image super-resolution (SR) refers to the reconstruction of a high-resolution image from its low-resolution version with the aid of a high-resolution image from another modality. Inspired by the recent success of recurrent neural networks in single image SR, we propose a novel multimodal recurrent convolutional neural network with coupled sparse priors for joint image SR. Our network fuses representations of the two image modalities at input layers using a learned multimodal convolutional sparse coding network. Additional recurrent convolutional stages are performed to further learn the mapping between the input modalities and the desired high-resolution estimate. We apply the proposed network to the tasks of near-infrared image SR and multi-spectral image SR using RGB images as the guidance modality. Experimental results show the superior performance of the proposed multimodal recurrent convolutional network against several state-of-the-art single-modal and multimodal image SR methods.

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Dr. Ir. Bruno Cornelis

+32 (0)02 629 167

bcorneli@etrovub.be

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Prof. Dr. Ir. Nikos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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