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- W4367190629 abstract "360{deg} omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower angular resolution due to being captured by fisheye lenses with the same sensor size for capturing planar images. To solve the above issues, we propose a two-stage framework for 360{deg} omnidirectional image superresolution. The first stage employs two branches: model A, which incorporates omnidirectional position-aware deformable blocks (OPDB) and Fourier upsampling, and model B, which adds a spatial frequency fusion module (SFF) to model A. Model A aims to enhance the feature extraction ability of 360{deg} image positional information, while Model B further focuses on the high-frequency information of 360{deg} images. The second stage performs same-resolution enhancement based on the structure of model A with a pixel unshuffle operation. In addition, we collected data from YouTube to improve the fitting ability of the transformer, and created pseudo low-resolution images using a degradation network. Our proposed method achieves superior performance and wins the NTIRE 2023 challenge of 360{deg} omnidirectional image super-resolution." @default.
- W4367190629 created "2023-04-28" @default.
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- W4367190629 date "2023-04-26" @default.
- W4367190629 modified "2023-10-16" @default.
- W4367190629 title "OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution" @default.
- W4367190629 doi "https://doi.org/10.48550/arxiv.2304.13471" @default.
- W4367190629 hasPublicationYear "2023" @default.
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