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Proceedings Paper

Nonlinear image restoration algorithm with artifact reduction
Author(s): Alan H. Lettington; Qi He Hong
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Paper Abstract

The distribution of edge values for an image of a general scene often has a sharp peak with a long tail. This property which can be well described by a Lorentzian probability function has been used to develop an efficient non-linear image restoration algorithm for reducing the various artifacts that often arise in the restored images. The algorithm starts with a Wiener filter solution which is used to model the edge image by the Lorentzian function so that the likelihood of the image can be estimated. A non-linear correction term is then introduced which increases this image likelihood under the mean square error (MSE) criterion. This process ensures that the resulting image retains its sharpness while reducing the noise and ringing artifacts. An iterative procedure has been developed to implement this method. Computer simulated results show that the algorithm is robust in reducing artifacts and easily implemented. The algorithm also possesses a superresolution capability due to the highly nonlinear property of the correction term.

Paper Details

Date Published: 21 September 1994
PDF: 10 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186530
Show Author Affiliations
Alan H. Lettington, Univ. of Reading (United Kingdom)
Qi He Hong, Univ. of Reading (United Kingdom)

Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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