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arXiv · 2312.07934

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
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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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Yuanbo Zhou, Yuyang Xue, Jiang Bi, Wenlin He, Xinlin Zhang, Jiajun Zhang, Wei Deng, Ruofeng Nie, Junlin Lan, Qinquan Gao, Tong Tong. 2023-12-13. Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information. https://arxiv.org/abs/2312.07934

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