Adaptive Regularized Restoration Algorithms Applied to HST Images
Aggelos K. Katsaggelos, Moon Gi Kang, and Mark R. Banham
Dept. of Electrical Engineering and Computer Science, Northwestern
University, Evanston, IL 60201-3118
This paper analyzes the performance of two set theoretic-based
iterative image restoration algorithms for Hubble Space Telescope
(HST) degraded images. The iterative adaptive constrained least
squares and frequency adaptive constrained least squares algorithms
are optimized here for HST data, and applied to several simulated and
real degraded HST images. Evaluations of both the flux linearity and
resolution enhancement of these algorithms are presented and compared
to results obtained by the Richardson-Lucy algorithm (Lucy 1974).
These results indicate that the iterative algorithms investigated here
are quite suitable for HST data, and provide excellent results in
terms of all evaluation criteria tested.