Super-resolution from unregistered omnidirectional images
This paper addresses the problem of super-resolution from low resolution spherical images that are not perfectly registered. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We first describe the impact of the registration error on the spherical Fourier transform coefficients. Then, we formulate the joint registration and reconstruction problem as a least squares norm minimization problem in the transform domain. Experimental results show that the proposed scheme leads to effective approximations of the high resolution images, even with large registration errors. 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.