Informed Choice of the LMS Parameters in Super-Resolution Video Reconstruction Applications

Super-resolution reconstruction of image sequences is highly dependent on data outliers and on the quality of the motion estimation. This paper addresses the design of the least mean square algorithm applied to super-resolution reconstruction (LMS-SRR). Based on a statistical model for the algorithm...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2008-02, Vol.56 (2), p.555-564
Main Authors: Costa, G.H., Bermudez, J.C.M.
Format: Article
Language:English
Subjects:
Adaptive filtering
Algorithm design and analysis
Algorithms
Applied sciences
Design engineering
Detection, estimation, filtering, equalization, prediction
Error analysis
Exact sciences and technology
Image processing
Image reconstruction
Image resolution
Image sequences
Information, signal and communications theory
least mean square (LMS)
Least squares approximation
Mathematical models
Miscellaneous
Motion estimation
Noise robustness
outliers
Phases
Reconstruction
registration error
Signal and communications theory
Signal processing
Signal processing algorithms
Signal resolution
Signal, noise
statistical analysis
Steady-state
Strategy
super-resolution
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Super-resolution reconstruction of image sequences is highly dependent on data outliers and on the quality of the motion estimation. This paper addresses the design of the least mean square algorithm applied to super-resolution reconstruction (LMS-SRR). Based on a statistical model for the algorithm behavior, we propose a design strategy to reduce the effects of outliers on the reconstructed image sequence. We show that the proposed strategy leads the algorithm to a close-to-optimum performance in both the transient and the steady-state phases of adaptation in practical situations in which registration errors occur. The analysis also shows that lower values of the step size do not necessarily lead to a better steady-state mean-square error, differently from the traditional LMS behavior.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2007.907910