We propose a novel algorithm for optical flow reconstruction, the Spectral Optical Flow Iterative Algorithm (SOFIA). It uses local structural information in color images. The reconstructed flow carries the total information contained in the images and the singularity of the inverse problem is potentially reduced when the spectral components are not mutually redundant. In addition, the method is extended to provide smoothening functionality by averaging the structural information represented by the structural tensor in local neighborhoods, avoiding thus gradient cancellation effects present when the image is directly smoothened. An iterative multi-scale scheme is proposed where the optical flow vector reconstructed at coarser scales is used to generate the source image for the reconstruction at finer scales. The algorithm is quantitatively validated with recorded images transformed with generated synthetic displacement vector fields. The dependence of reconstruction accuracy on the parameters of both the images and the vector deformation fields is presented.
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