Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
CoRR(2023)
摘要
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}.
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