Unsupervised, information-theoretic, adaptive image filtering for image restoration
Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be e...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 28(2006), 3 vom: 09. März, Seite 364-76 |
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| Format: | Aufsatz |
| Sprache: | English |
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2006
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | Image restoration is an important and widely studied problem in computer vision and image processing. Various image filtering strategies have been effective, but invariably make strong assumptions about the properties of the signal and/or degradation. Hence, these methods lack the generality to be easily applied to new applications or diverse image collections. This paper describes a novel unsupervised, information-theoretic, adaptive filter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing their joint entropy. In this way, UINTA automatically discovers the statistical properties of the signal and can thereby restore a wide spectrum of images. The paper describes the formulation to minimize the joint entropy measure and presents several important practical considerations in estimating neighborhood statistics. It presents a series of results on both real and synthetic data along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing |
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| Beschreibung: | Date Completed 31.03.2006 Date Revised 10.12.2019 published: Print Citation Status MEDLINE |
| ISSN: | 0162-8828 |