SIGNAL: Stereo Image Neural Learner for Continuous Super-Resolution

Stereo images are widely used in many scenarios such as autonomous driving and stereoscopic display. Stereo image super-resolution is a common technique to obtain high-definition stereo images. However, existing methods are mainly designed for fixed scales and cannot smoothly support arbitrary upscaling. To address these problems, this paper proposes a Stereo ImaGe NeurAl Learner (SIGNAL) based on implicit neural representation, which consists of three parts. First, a feature extraction module leveraging the iPASSR architecture to robustly extract hierarchical features from low-resolution stereo images. Second, a novel stereo implicit representation module that explicitly models the inherent geometric consistency and contextual relationships between the two views, enhancing the latent features through view-specific and cross-view interactions. Third, a cross-view decoding module designed to fully exploit the complementary information across views during the image reconstruction process. Extensive experiments across diverse datasets demonstrate that the proposed SIGNAL framework achieves superior performance. The results underscore the effectiveness of our approach in handling arbitrary scaling factors while preserving visual fidelity in stereo image restoration.