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

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 furt...

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Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Guo, Hansheng, Li, Juncheng, Gao, Guangwei, Li, Zhi, Zeng, Tieyong
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Acoustics
Adaptation models
Feature extraction
Parallax Fusion Transformer
Source coding
Speech processing
SSR
Stereo Cross Attention
Stereo Image Super-Resolution
Superresolution
Transformers
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Summary: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 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. 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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096174