LEARNED MULTIMODAL CONVOLUTIONAL SPARSE CODING FOR GUIDED IMAGE SUPER-RESOLUTION

The success of deep learning in various tasks, including solving inverse problems, has triggered the need for designing deep neural networks that incorporate domain knowledge. In this paper, we design a multimodal deep learning architecture for guided image super-resolution, which refers to the problem of super-resolving a low-resolution image with the aid of a high-resolution image of another modality. The proposed architecture is based on a novel deep learning model, obtained by unfolding a proximal method that solves the problem of convolutional sparse coding with side information. We applied the proposed architecture to super-resolve near-infrared images using RGB images as side information. Experimental results report average PSNR gains of up to 2.85 dB against state-of-the-art multimodal deep learning and sparse coding models.