Two techniques for image restoration are compared in this paper. One is a technique based on the theory of optimal adaptive stack filtering; the other is a recently developed vector detection approach to image restoration. The primary difference between these two techniques is that the optimal detection technique exploits multilevel a priori information, while the stack filter uses only single level information. Both approaches have very similar design constraints: (a) both rely on the existence of a training sequence for the image source in order to obtain optimal processing; (b) the underlying random fields need not be stationary and a direct computation of the statistics of the desired images is not required. Adaptive stack filters do, however, require a training set of the noise while the optimal detection approach only needs a multivariate parametric representation. The image restoration performance of these two methods is compared in a signal dependent noise environment characterizing imaging systems with speckle, film-grain, and Poisson shot noise. Comparisons are made using the Mean Absolute Error measure as well as a subjective measure.

Date Published: 1 July 1990
PDF: 12 pages
Proc. SPIE 1247, Nonlinear Image Processing, (1 July 1990); doi: 10.1117/12.19611

Published in SPIE Proceedings Vol. 1247:
Nonlinear Image Processing
Edward J. Delp, Editor(s)