JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS

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.