Effective 360° imaging requires a very high resolution because the field of view is extraordinarily high. Single-image super-resolution (SISR) applied to 360° imaging has the potential to solve the resolution/quality problem in this modality. In this paper, we exploit existing perspective SISR networks to address this problem by (1) introducing a distortion map as an additional input with the $360^{\circ}-$ distortion-aware loss function, and (2) augmenting the training 360° images by distorting the perspective images. We also present a new 360° image dataset from YouTube for training. Our extensive experiments show that how each component contributes to the better transfer from the perspective domain to the 360° domain and merging all the ideas leads to the best performance in quantitative and qualitative ways for the 360° SISR task.