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Proceedings Paper

Information, language, and pixon-based image reconstruction
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Paper Abstract

From an information theoretic point of view, the inverse problem and the problem of data compression are intimately related. Optimal compression seeks the most concise representation of a data set, while Bayesian probability theory favors image reconstruction algorithms which minimally model the information present in the data. This should not be surprising. It is in keeping with a scientist's intuitive need to satisfy the precepts of Occam's Razor, i.e. not to over interpret one's data. Information scientists might describe this process as quantifying the algorithmic information content (AIC) of the image, and then using this 'coordinate system' for optimal image reconstruction. The present paper describes pixon- based image reconstruction, a technique based upon AIC minimal image models. Because AIC is language dependent (description length and language complexity are inversely related) we have based the practical implementation of our method on concise (descriptive) languages for generic images, e.g. multiresolution basis functions. The present paper describes both the theory of pixon-based reconstruction and presents practical examples demonstrating that pixon-based reconstruction produces results consistently superior (often by large factors) to those of other methods, including the best examples of maximum likelihood and maximum entropy image reconstruction.

Paper Details

Date Published: 25 October 1996
PDF: 20 pages
Proc. SPIE 2827, Digital Image Recovery and Synthesis III, (25 October 1996); doi: 10.1117/12.255082
Show Author Affiliations
Richard Charles Puetter, Univ. of California/San Diego (United States)

Published in SPIE Proceedings Vol. 2827:
Digital Image Recovery and Synthesis III
Paul S. Idell; Timothy J. Schulz, Editor(s)

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