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The 360{\deg}imaging has recently gained great attention; however, itsangular resolution is relatively lower than that of a narrow field-of-view(FOV) perspective image as it is captured by using fisheye lenses with the samesensor size. Therefore, it is beneficial to super-resolve a 360{\deg}image.Some attempts have been made but mostly considered the equirectangularprojection (ERP) as one of the way for 360{\deg}image representation despite oflatitude-dependent distortions. In that case, as the output high-resolution(HR)image is always in the same ERP format as the low-resolution (LR) input,another information loss may occur when transforming the HR image to otherprojection types. In this paper, we propose SphereSR, a novel framework togenerate a continuous spherical image representation from an LR 360{\deg}image,aiming at predicting the RGB values at given spherical coordinates forsuper-resolution with an arbitrary 360{\deg}image projection. Specifically, wefirst propose a feature extraction module that represents the spherical databased on icosahedron and efficiently extracts features on the sphericalsurface. We then propose a spherical local implicit image function (SLIIF) topredict RGB values at the spherical coordinates. As such, SphereSR flexiblyreconstructs an HR image under an arbitrary projection type. Experiments onvarious benchmark datasets show that our method significantly surpassesexisting methods.

SphereSR: 360{\deg} Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation