Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT. Codes and models will be available at https://github.com/Fanghua-Yu/OSRT.

翻译：全向图像为沉浸式体验提供了重要研究价值。尽管全向图像需要极高分辨率以捕捉全景场景细节，但目前多数全向图像分辨率尚不充足。已有方法尝试通过等距柱状投影图像的超分辨率重建解决该问题，但它们在降质过程中忽略了等距柱状投影的几何特性，导致模型难以泛化至真实等距柱状投影图像。本文提出鱼眼下采样方法，通过模拟真实成像过程生成更逼真的低分辨率样本。进而设计畸变感知Transformer（OSRT），以连续自适应方式调节等距柱状投影畸变。无需复杂流程，OSRT在峰值信噪比上较已有方法提升约0.2dB。此外，我们提出便捷的数据增强策略，从普通图像合成伪等距柱状投影图像。该简单策略可缓解大型网络过拟合问题，显著提升全向图像超分辨率性能。大量实验证明OSRT达到当前最优水平。代码与模型将发布在https://github.com/Fanghua-Yu/OSRT。