A statistical unification of image interpolation, error concealment, and source-adapted filter design

Author

Muhlich, M. ; Mester, Rudolf

Author_Institution

Image & Vision Group, Inst. for Appl. Phys., Frankfurt Am Main, Germany

fYear

2004

fDate

28-30 March 2004

Firstpage

128

Lastpage

132

Abstract

The natural characteristics of image signals and the statistics of measurement noise are decisive for designing optimal filter sets and optimal estimation methods in signal processing. Astonishingly, this principle has so far only partially found its way into the field of image sequence processing. We show how a Wiener-type MMSE optimization criterion for the resulting image signal, based on a simple covariance model of images or image sequences, provides direct and intelligible solutions for various, apparently different, problems, such as error concealment, or adaption of filters to signal and noise statistics.

Keywords

FIR filters; Wiener filters; adaptive filters; covariance analysis; covariance matrices; error correction; image processing; image sequences; interpolation; least mean squares methods; optimisation; random noise; MMSE optimization criterion; Wiener filtering; Wiener-type optimization criterion; covariance matrices; covariance model; error concealment; image interpolation; image sequence processing; image signals; linear FIR filters; linear filters; measurement noise statistics; optimal estimation methods; optimal filter sets; signal processing; source-adapted filter design; statistical unification; Filtering theory; Finite impulse response filter; Image sequences; Interpolation; Noise figure; Nonlinear filters; Physics; Signal design; Statistics; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Interpretation, 2004. 6th IEEE Southwest Symposium on

Print_ISBN

0-7803-8387-7

Type

conf

DOI

10.1109/IAI.2004.1300959

Filename

1300959