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{this https URL}{link}.

本论文提出了一种新颖的方法，通过整合隐式立体信息辨别器和混合降质模型来弥补现实世界立体域中复杂降质与现实世界单幅图像超分辨率领域中简单降质之间的差异，确保有效增强并保持视差一致性。