New approaches for space-invariant image restoration
- 1 January 1993
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 261-264 vol.5
- https://doi.org/10.1109/icassp.1993.319797
Abstract
The problem of restoring images degraded by space-invariant blurs and noise is addressed. Two approaches, one based on Kalman filtering and the other on projection onto convex sets (POCS), are proposed. The Kalman filtering approach modifies the image model used in the usual reduced-order model Kalman filtering (ROMKF) approach to obtain a more accurate representation of the image distribution. The proposed POCS-based approach utilizes novel space-domain constraints defined in terms of the space-varying blur function. Both approaches have been shown to effectively restore images degraded by LSV (linear space-variant) blur functions in the presence of additive noise.Keywords
This publication has 6 references indexed in Scilit:
- An overview of convex projections theory and its application to image recovery problemsUltramicroscopy, 1992
- Prototype image constraints for set-theoretic image restorationIEEE Transactions on Signal Processing, 1991
- Survey of recent developments in digital image restorationOptical Engineering, 1990
- High-resolution image recovery from image-plane arrays, using convex projectionsJournal of the Optical Society of America A, 1989
- Image restoration using reduced order modelsSignal Processing, 1989
- The feasible solution in signal restorationIEEE Transactions on Acoustics, Speech, and Signal Processing, 1984