The 360 imaging has recently gained great attention; however, its angular 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 same sensor size. Therefore, it is beneficial to super-resolve a 360 image. Some attempts have been made but mostly considered the equirectangular projection (ERP) as one of the way for 360 image representation despite of latitude-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 other projection types.In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360 image, aiming at predicting the RGB values at given spherical coordinates for super-resolution with an arbitrary 360 image projection. Specifically, we first pro-pose a feature extraction module that represents the spherical data based on icosahedron and efficiently extracts features on the spherical surface. We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates. As such, SphereSR flexibly re-constructs an HR image under an arbitrary projection type.Experiments on various benchmark datasets show that our method significantly surpasses existing methods.

提出了SphereSR框架，可通过一个特征提取模块和一个球形本地隐式图像函数来灵活地从LR 360度图像生成高分辨率图像，从而实现在任意投影类型下的超分辨率重建，并在多种基准数据集上证明其显著优于现有的方法。

SphereSR：通过连续球面图像表示进行任意投影的360°图像超分辨率