In the recent past considerable attention has been devoted to the application of Kalman filtering to smoothing out observation noise in image data. Optimal two-dimensional Kalman filtering algorithms require large amounts of storage and computation. Thus, the study of suboptimum estimators that require less computation is of importance. A comparison of some suboptimum image filters against the optimum non-recursive interpolator is accomplished. A new semi-causal (hybrid) filter is proposed that compensates the suboptimality of a simple two-dimensional recursive filter by means of an optimal combination of its estimate and a few non-causal observations.

Date Published: 28 December 1979
PDF: 9 pages
Proc. SPIE 0207, Applications of Digital Image Processing III, (28 December 1979); doi: 10.1117/12.958232

Published in SPIE Proceedings Vol. 0207:
Applications of Digital Image Processing III
Andrew G. Tescher, Editor(s)