This paper addresses the reconstruction of high resolution omnidirectional images from multiple low resolution images with inexact registration. When omnidirectional images from low resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform domain super-resolution problem in the spherical imaging framework. We describe how several spherical images with arbitrary rotations in the SO(3) rotation group contribute to the reconstruction of a high resolution image with help of the Spherical Fourier Transform (SFT). As low resolution images might not be perfectly registered in practice, the impact of inaccurate alignment on the transform coefficients is further analyzed. We then cast the joint registration and super-resolution problem as a total least squares norm minimization problem in the SFT domain. A l1- regularized total least squares problem is also considered. The regularized problem is solved efficiently by interior point methods. Experiments with synthetic and natural images show that the proposed scheme leads to effective reconstruction of high resolution images even when large registration errors exist in the low resolution images. The quality of the reconstructed images also increases rapidly with the number of low resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes. Finally, we highlight the benefit of the additional regularization constraint that clearly leads to reduced noise and improved reconstruction quality.