PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution

• URL: http://arxiv.org/abs/2303.13807v1
• Date: Fri, 24 Mar 2023 05:04:52 GMT
• Title: PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
• Authors: Hansheng Guo, Juncheng Li, Guangwei Gao, Zhi Li, Tieyong Zeng
• Abstract summary: We propose a novel Transformer-based parallax fusion module called Parallax Fusion Transformer (PFT) PFT employs a Cross-view Fusion Transformer (CVFT) to utilize cross-view information and an Intra-view Refinement Transformer (IVRT) for intra-view feature refinement. Experiments and ablation studies show that PFT-SSR achieves competitive results and outperforms most SOTA methods.
• Score: 22.251884516076096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous methods have achieved promising results, they did not fully utilize the information of cross-view and intra-view. To further unleash the potential of binocular images, in this letter, we propose a novel Transformerbased parallax fusion module called Parallax Fusion Transformer (PFT). PFT employs a Cross-view Fusion Transformer (CVFT) to utilize cross-view information and an Intra-view Refinement Transformer (IVRT) for intra-view feature refinement. Meanwhile, we adopted the Swin Transformer as the backbone for feature extraction and SR reconstruction to form a pure Transformer architecture called PFT-SSR. Extensive experiments and ablation studies show that PFT-SSR achieves competitive results and outperforms most SOTA methods. Source code is available at https://github.com/MIVRC/PFT-PyTorch.

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