On the application of robust functionals in regularized image restoration

Michael E. Zervakis, Taek M Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

This paper considers the aspects of robust M-estimation in regularized image restoration. Robust functionals are employed to allow the representation and efficient suppression of a wide variety of noise processes and permit the reconstruction of sharp edges. A new class of robust entropic functionals is introduced, which operates only on the high-frequency content of the signal and reflects sharp deviations in the signal distribution. This class can also incorporate prior structural information regarding the original image, in a way similar to the maximum information principle. The properties of robust algorithms are demonstrated through restoration examples in different noise environments.

Original languageEnglish (US)
Title of host publicationImage and Multidimensional Signal Processing
PublisherPubl by IEEE
Volume5
ISBN (Print)0780309464
StatePublished - Jan 1 1993
EventIEEE International Conference on Acoustics, Speech and Signal Processing, Part 5 (of 5) - Minneapolis, MN, USA
Duration: Apr 27 1993Apr 30 1993

Other

OtherIEEE International Conference on Acoustics, Speech and Signal Processing, Part 5 (of 5)
CityMinneapolis, MN, USA
Period4/27/934/30/93

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