Reliable Attention Based Stereo Image Super-Resolution Network

Stereo image super-resolution (StereoSR) exploits complementary information between paired views to enhance image reconstruction quality. Existing attention-based methods for cross-view feature interaction overlook a fundamental principle: effective stereo reconstruction requires compensating insufficient features in one view with reliable features from its counterpart. This observation raises a critical question of quantifying and leveraging feature reliability. To address this, we propose a Reliable Attention Based Stereo Image Super-resolution Network (RASSR), which explicitly models and utilizes feature uncertainty for guided cross-view interaction. The core component of RASSR is the Reliable Stereo Cross Attention Module (RSCAM), which dynamically identifies high-uncertainty regions in one view and adaptively selects complementary features from low-uncertainty regions in the alternative view. To support this uncertainty-driven interaction, we propose two key components: a Monte Carlo Feature Extraction Block (MCFEBlock) that quantifies feature uncertainty through Monte Carlo sampling, and a Feature Compensation Module (FCM) that mitigates information loss during the sampling process. Extensive experiments on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets demonstrate that, by enhancing attention reliability, RASSR achieves state-of-the-art performance, outperforming existing methods in both PSNR and SSIM metrics.