Potentials and Limits of Super-Resolution Algorithms and Signal Reconstruction from Sparse Data
This work addresses image enhancement and signal reconstruction problems, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper investigates the performance of super-resolution algorithms using subpixel elastic registration to enhance image resolution from video frames and explores signal reconstruction from sparse samples under various basis functions.
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.