Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach
This work addresses super-resolution image reconstruction for scenes with projective transformations, but it is incremental as it builds on existing methods like Total Variation and Majorization-Minimization without introducing a new paradigm.
The paper tackled the problem of reconstructing super-resolution images from sequences using Total Variation regularity and a Majorization-Minimization approach, achieving improved restoration from motion measurements as demonstrated in simulations, though it did not provide real-time results.
This work aims to reconstruct image sequences with Total Variation regularity in super-resolution. We consider, in particular, images of scenes for which the point-to-point image transformation is a plane projective transformation. We first describe the super-resolution image's imaging observation model, an interpolation and Fusion estimator, and Projection on Convex Sets. We explain motion and compute the optical flow of a sequence of images using the Horn-Shunck algorithm to estimate motion. We then propose a Total Variation regulazer via a Majorization-Minimization approach to obtain a suitable result. Super Resolution restoration from motion measurements is also discussed. Finally, the simulation's part demonstrates the power of the proposed methodology. As expected, this model does not give real-time results, as seen in the numerical experiments section, but it is the cornerstone for future approaches. Finally, the simulation's part demonstrates the power of the proposed methodology. As expected, this model does not give real-time results, as seen in the numerical experiments section, but it is the cornerstone for future approaches.