Coupled Ista Network for Multi-modal Image Super-resolution

In this paper, we propose a novel deep neural network architecture for multi-modal image super-resolution (MISR). The architecture is based on a new joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency and to map them to a high-resolution version of one modality. In JMDL, we learn three dictionaries and two transform matrices to combine the modalities. By using the learned model, we then design the network architecture by a coupled unfolding of the iterative shrinkage and thresholding algorithm (ISTA). We finally initialize the parameters of our network with a new optimization strategy. The initialized parameters are demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. The numerical results show that our method outperforms other state-of-the-art methods quantitatively and qualitatively for MISR.