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Spatially adaptive iterative algorithm for the restoration of astronomical images
Author(s) -
Katsaggelos Aggelos K.,
Kang Moon Gi
Publication year - 1995
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.1850060404
Subject(s) - smoothing , weighting , image restoration , algorithm , regularization (linguistics) , iterative method , convergence (economics) , computer science , image (mathematics) , convexity , minification , noise (video) , mathematics , mathematical optimization , image processing , artificial intelligence , computer vision , medicine , economic growth , financial economics , economics , radiology
This article develops an iterative spatially adaptive regularized image restoration algorithm. The proposed algorithm is based on the minimization of a weighted smoothing functional. The weighting matrices are defined as functions of the partially restored image at each iteration step. As a result, no prior knowledge about the image and the noise is required, but the weighting matrices as well as the regularization parameter are updated based on the restored image at every step. Conditions for the convexity of the weighted smoothing functional and for the convergence of the iterative algorithm are established for a unique global solution which does not depend on initial conditions. Experimental results are shown with astronomical images which demonstrate the effectiveness of the proposed algorithm.