Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to improve stereo images, these methods often introduce notable modifications to the inherent disparity, resulting in a loss in the consistency of disparity between the original and the enhanced stereo images. To overcome this limitation, this paper proposes a novel approach that integrates a implicit stereo information discriminator and a hybrid degradation model. This combination ensures effective enhancement while preserving disparity consistency. The proposed method bridges the gap between the complex degradations in real-world stereo domain and the simpler degradations in real-world single-image super-resolution domain. Our results demonstrate impressive performance on synthetic and real datasets, enhancing visual perception while maintaining disparity consistency. The complete code is available at the following \href{https://github.com/fzuzyb/SCGLANet}{link}.

翻译：真实世界的立体图像超分辨率对提升计算机视觉系统性能具有重要影响。尽管现有的单图像超分辨率方法可应用于立体图像增强，但这些方法往往会显著改变固有的视差，导致原始与增强后立体图像间的视差一致性损失。为克服这一局限，本文提出一种融合隐式立体信息判别器与混合退化模型的新方法。该组合策略在保持视差一致性的同时实现有效增强。所提出的方法弥合了真实世界立体域中复杂退化与真实世界单图像超分辨率域中简单退化之间的差距。实验结果表明，本文方法在合成数据集与真实数据集上均展现出优异性能，在维持视差一致性的同时提升了视觉感知质量。完整代码已发布于以下链接：\href{https://github.com/fzuzyb/SCGLANet}{链接}。