Optimization of regularization operators for adaptive least-squares image restoration

摘要

This paper presents regularized least squares algorithms for the restoration and reconstruction of images. Whitening filters of short length are derived formally as optimal regularization operators. Adaptive versions of the algorithms are developed by matching a weighting function to the particular regularization function. The adaptive regularization leads to proper noise suppression as well as to enhanced resolution of discontinuities. The application focuses on the restoration of images recorded by the Hubble Space Telescope (HST).