PanoExtend: An omnidirectional image super-resolution method based on spherical expansion

The high resolution of omnidirectional images (ODIs) results in substantial acquisition costs, a challenge that can be alleviated via super-resolution techniques. Mainstream approaches to 2D planar image super-resolution convert images into 1D sequences via row-wise concatenation, achieving impressive performance. However, directly applying such dimension-reduction pipeline to ODIs suffers from projection distortion. To address this issue, this paper proposes PanoExpand, a method that facilitates super-resolution by bidirectionally converting between ODIs and 1D sequences. Based on spherical projection, PanoExpand transforms ODIs into 1D sequences from a 3D perspective, effectively eliminating inherent distortions while preserving spatial continuity. Specifically, PanoExpand takes an omnidirectional image as input, converts it into a 1D sequence via spherical unfolding, then iteratively refines the sequence through learning and training, and finally reconstructs it into a 2D image to obtain the super-resolved panoramic output. Experimental results demonstrate the superiority of PanoExpand in omnidirectional image super-resolution quality.