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Real-world stereo image super-resolution has a significant influence onenhancing the performance of computer vision systems. Although existing methodsfor 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 andthe enhanced stereo images. To overcome this limitation, this paper proposes anovel approach that integrates a implicit stereo information discriminator anda hybrid degradation model. This combination ensures effective enhancementwhile preserving disparity consistency. The proposed method bridges the gapbetween the complex degradations in real-world stereo domain and the simplerdegradations in real-world single-image super-resolution domain. Our resultsdemonstrate impressive performance on synthetic and real datasets, enhancingvisual perception while maintaining disparity consistency. The complete code isavailable at the following \href{https://github.com/fzuzyb/SCGLANet}{link}.

Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information