Title :
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
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;
Conference_Titel :
Image Analysis and Interpretation, 2004. 6th IEEE Southwest Symposium on
Print_ISBN :
0-7803-8387-7
DOI :
10.1109/IAI.2004.1300959
