Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach

TL;DR

This paper introduces a super-resolution image reconstruction method using total variation regularization and a majorization-minimization approach, demonstrating its effectiveness through simulations.

Contribution

It proposes a novel total variation-based super-resolution reconstruction method employing a majorization-minimization algorithm, specifically addressing motion and optical flow estimation.

Findings

Effective super-resolution reconstruction demonstrated in simulations

Method outperforms traditional approaches in quality

No real-time performance achieved, but foundational for future work

Abstract

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.