Toward Real World Stereo Image Super-Resolution via Hybrid Degradation
  Model and Discriminator for Implied Stereo Image Information
Yuanbo Zhou Yuyang Xue Jiang Bi Wenlin He Xinlin Zhang Jiajun Zhang Wei Deng Ruofeng Nie Junlin Lan Qinquan Gao Tong Tong
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}.