mlsanityData to 14 Oct 2026

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

arXiv 2312.07934 ↗First posted 13 Dec 2023eess.IV cs.CVcited by 2

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Abstract (arXiv)

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 hrefhttps://github.com/fzuzyb/SCGLANetlink.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 46 of its 68 references that are in the map. First posted on arXiv 13 Dec 2023.

Builds on

46 of 68 references.

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34 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolutioncited by 1
  2. 2025Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospectscited by 1