Adaptive Regularized Restoration Algorithms Applied to HST Images

Aggelos K. Katsaggelos, Moon Gi Kang, and Mark R. Banham


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.


Pages 3-13


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