Models used to derive image reconstruction algorithms typically make assumptions designed to increase the
computational tractability of the algorithms while taking enough account of the physics to achieve desired
performance. As the models for the physics become more detailed, the algorithms typically increase in complexity,
often due to increases in the number of parameters in the models. When parameters are estimated from measured
data and models of increased complexity include those of lower complexity as special cases, then as the number of
parameters increases, model errors decrease and estimation errors increase. We adopt an information geometry
approach to quantify the loss due to model errors and Fisher information to quantify the loss due to estimation
errors. These are unified into one cost function. This approach is detailed in an X-ray transmission tomography
problem where allmodels are approximations to the underlying problem defined on the continuum. Computations
and simulations demonstrate the approach. The analysis provides tools for determining an appropriate model
complexity for a given problem and bounds on information that can be extracted.
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