Recurrent Interaction Network for Stereoscopic Image Super-Resolution

Recently, deep learning-based stereoscopic image super-resolution has attracted extensive attention and made great progress. However, existing methods have not adequately explored the inter-view dependency among two-view multi-level features. In this paper, a recurrent interaction network for stereoscopic image super-resolution (RISSRnet) is proposed to learn the inter-view dependency. To efficiently utilize the relationship between the two views, a recurrent interaction module is designed to achieve recurrent interaction among two-view multi-level features from the regrouped sequences, which are generated by a coupled queue-regroup mechanism. In addition, to recursively enhance features in the recurrent interaction module, an iterative propagation strategy is developed for sufficient interaction. Extensive experimental results demonstrate the effectiveness and superiority of the proposed RISSRnet.