Symmetric Parallax Attention for Stereo Image Super-Resolution
This work addresses the problem of enhancing resolution in stereo images for applications like 3D vision, but it is incremental as it builds on existing stereo SR methods.
The paper tackles stereo image super-resolution by exploiting symmetry cues in stereo image pairs, achieving superior performance on four public datasets.
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method. Source code is available at https://github.com/YingqianWang/iPASSR.