Information-theoretic image formation

• Published in: IEEE transactions on information theory Vol. 44; no. 6; pp. 2094 - 2123
• Main Authors: O'Sullivan, J.A., Blahut, R.E., Snyder, D.L.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.1998; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data imaging; Image analysis; Image processing; Image reconstruction; Image restoration; Image sensors; Information technology; Information theory; Minimization methods; State estimation; Stochastic processes; Testing; Theory
• ISSN: 0018-9448; 1557-9654
• DOI: 10.1109/18.720533

The emergent role of information theory in image formation is surveyed. Unlike the subject of information-theoretic communication theory, information-theoretic imaging is far from a mature subject. The possible role of information theory in problems of image formation is to provide a rigorous framework for defining the imaging problem, for defining measures of optimality used to form estimates of images, for addressing issues associated with the development of algorithms based on these optimality criteria, and for quantifying the quality of the approximations. The definition of the imaging problem consists of an appropriate model for the data and an appropriate model for the reproduction space, which is the space within which image estimates take values. Each problem statement has an associated optimality criterion that measures the overall quality of an estimate. The optimality criteria include maximizing the likelihood function and minimizing mean squared error for stochastic problems, and minimizing squared error and discrimination for deterministic problems. The development of algorithms is closely tied to the definition of the imaging problem and the associated optimality criterion. Algorithms with a strong information-theoretic motivation are obtained by the method of expectation maximization. Related alternating minimization algorithms are discussed. In quantifying the quality of approximations, global and local measures are discussed. Global measures include the (mean) squared error and discrimination between an estimate and the truth, and probability of error for recognition or hypothesis testing problems. Local measures include Fisher information.