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

Book Contribution - Book Chapter Conference Contribution

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.
Book: IEEE International Conference on Image Processing (ICIP)
Series: Proceedings - International Conference on Image Processing, ICIP
Pages: 868-872
Number of pages: 5
Publication year:2020
Accessibility:Closed