  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.
