Super-Resolution (SR) has gained increasing research attention over the past few years. With the development of Deep Neural Networks (DNNs), many super-resolution methods based on DNNs have been proposed. Although most of these methods are aimed at ordinary frames, there are few works on super-resolution of omnidirectional frames. In these works, omnidirectional frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular Projection (ERP). Although ERP has been widely used for projection, it has severe projection distortion near poles. Current DNN-based SR methods use 2D convolution modules, which is more suitable for the regular grid. In this paper, we find that different projection methods have great impact on the performance of DNNs. To study this problem, a comprehensive comparison of projections in omnidirectional super-resolution is conducted. We compare the SR results of different projection methods. Experimental results show that Equi-Angular cube map projection (EAC), which has minimal distortion, achieves the best result in terms of WS-PSNR compared with other projections. Code and data will be released.
翻译：过去几年中，超分辨率（SR）受到了越来越多的研究关注。随着深度神经网络（DNN）的发展，许多基于DNN的超分辨率方法已被提出。虽然这些方法大多针对普通帧，但对于全方位帧的超分辨率研究还很少。在这些研究中，全息图像被通过等距矩形投影（ERP）从三维球面投影到二维平面上。虽然ERP已广泛用于投影，但在极点附近存在严重的投影畸变。当前的基于DNN的SR方法使用二维卷积模块，这对于规则网格更加适用。在本文中，我们发现不同的投影方法对DNN的性能有很大的影响。为了研究这个问题，我们进行了全方位超分辨率投影方式的综合比较。我们比较了不同投影方法的SR结果。实验结果表明，具有最小畸变的等角立方体映射投影（EAC）在WS-PSNR方面与其他投影方法相比实现了最佳结果。代码和数据将公开发布。
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