Strong-Weak Cross-View Interaction Network for Stereo Image Super-Resolution

Recently, super-resolution (SR) performance has been improved by the stereo images since the beneficial information could be provided by another view. Transformer has shown significant performance gains for computer vision tasks, while it needs huge computing resources and training time. To alleviate this problem, we introduce an efficient Transformer feature extraction block, which can efficiently capture long-range pixel interactions with lower resource consumption. There are many kinds of cross-view interaction modules for stereo image SR, and they all have limitations of SR performance in their own models. To address the aforementioned challenge, we first propose the strong-weak cross-view interaction mechanism, which consists of strong cross-view interaction module and weak cross-view interaction module. Benefiting from the proposed mechanism, the SR performance can be improved significantly with a negligible increment of computing cost. We integrate the efficient Transformer feature extraction module and the strong-weak cross-view interaction mechanism into a unified framework named strong-weak cross-view interaction network (SWCVIN), and extensive experiments on three benchmark datasets show the proposed model achieves state-of-the-art results.