Toward Real World Stereo Image Super-Resolution via Hybrid Degradation
Model and Discriminator for Implied Stereo Image Information
- URL: http://arxiv.org/abs/2312.07934v1
- Date: Wed, 13 Dec 2023 07:24:50 GMT
- Title: Toward Real World Stereo Image Super-Resolution via Hybrid Degradation
Model and Discriminator for Implied Stereo Image Information
- Authors: Yuanbo Zhou, Yuyang Xue, Jiang Bi, Wenlin He, Xinlin Zhang, Jiajun
Zhang, Wei Deng, Ruofeng Nie, Junlin Lan, Qinquan Gao, and Tong Tong
- Abstract summary: Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems.
Existing methods for single-image super-resolution can be applied to improve stereo images.
This paper proposes a novel approach that integrates a implicit stereo information discriminator and a hybrid degradation model.
- Score: 10.957275128743529
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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