Potentials and Limits of Super-Resolution Algorithms and Signal Reconstruction from Sparse Data

TL;DR

This paper explores the potential and limitations of super-resolution algorithms and signal reconstruction from sparse data, focusing on video stabilization techniques and discrete signal recovery under various basis functions.

Contribution

It investigates the effectiveness of subpixel elastic registration for super-resolution and compares reconstruction capabilities across multiple basis functions.

Findings

Elastic registration can enhance video resolution using chaotic displacements.

Reconstruction quality varies with basis functions like DFT, Haar, and wavelets.

Super-resolution methods have inherent limits depending on data sparsity and basis choice.

Abstract

A common distortion in videos is image instability in the form of chaotic (global and local displacements). Those instabilities can be used to enhance image resolution by using subpixel elastic registration. In this work, we investigate the performance of such methods over the ability to improve the resolution by accumulating several frames. The second part of this work deals with reconstruction of discrete signals from a subset of samples under different basis functions such as DFT, Haar, Walsh, Daubechies wavelets and CT (Radon) projections.