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30 April 1992 Modeling, processing, and data compression for spatially invariant linearly additive image sequences
John W. V. Miller, James B. Farison, Youngin O. Shin
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Proceedings Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures; (1992) https://doi.org/10.1117/12.57918
Event: Robotics '91, 1991, Boston, MA, United States
Abstract
A class of image sequences can be characterized as being spatially invariant and linearly additive based on their image formation processes. In these kinds of sequences, all features are positionally invariant in each image of a given sequence but have varying gray-scale properties. The various features of the scene contribute additively to each image of the sequence but the image-formation processes associated with given features have characteristic signatures describing the manner in which they vary over the image sequence. Examples of appropriate image sequences include multispectral image sequences, certain temporal image sequences, and NMR image sequences generated by modification of the excitation parameters. Note that image sequences can be formed using a variety of imaging modalities as long as the linearly additive and spatially invariant requirements are not violated. Features associated with different image-formation processes generally will have unique signatures that can be used to generate linear filters for isolating selected image-formation processes or for performing data compression. Starting with an explicit mathematical model, techniques are presented for generating optimal filters using simultaneous diagonalization for enhancement of desired image-formation processes and data compression with this class of image sequences. A unique property of this approach is that even if several image-formation processes occupy a given pixel, they can still be isolated.
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John W. V. Miller, James B. Farison, and Youngin O. Shin "Modeling, processing, and data compression for spatially invariant linearly additive image sequences", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57918
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KEYWORDS
Image processing
Image filtering
Fusion energy
Image fusion
Digital filtering
Image compression
Data compression
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