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arXiv · 2304.13471

OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution

Xiaopeng Sun·Weiqi Li·Zhenyu Zhang·Qiufang Ma·Xuhan Sheng·Ming Cheng·Haoyu Ma·Shijie Zhao·Jian Zhang·Junlin Li·Li Zhang
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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.

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Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Qiufang Ma, Xuhan Sheng, Ming Cheng, Haoyu Ma, Shijie Zhao, Jian Zhang, Junlin Li, Li Zhang. 2023-04-26. OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution. https://arxiv.org/abs/2304.13471

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