  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.
