Divergence penalty for image regularization

This paper discusses a new roughness penalty for use in estimation problems including image estimation problems. It is one of a new class of penalty functions for use in estimation and image regularization that has recently been proposed. These functions penalize the discrepancy between an image and a shifted version of itself; here the discrepancy measure is the I-divergence. This penalty is closely related to a penalty used for density estimation that was introduced by Good and Gaskins. Roughness penalty methods form an attractive alternative to Markov random field priors, achieving many of the same properties including the introduction of neighborhood structures. An example of the use of this new penalty for radar imaging using real radar data has been examined.