A scale invariant Bayesian method to solve linear inverse problems

In this paper we propose a new Bayesian estimation method to solve linear inverse problems in signal and image restoration and reconstruction problems which has the property to be scale invariant. In general, Bayesian estimators are nonlinear functions of the observed data. The only exception is the Gaussian case. When dealing with linear inverse problems the linearity is sometimes a too strong property, while scale invariance often remains a desirable property. As everybody knows one of the main difficulties with using the Bayesian approach in real applications is the assignment of the direct (prior) probability laws before applying the Bayes' rule. We discuss here how to choose prior laws to obtain scale invariant Bayesian estimators. In this paper we discuss and propose a family of generalized exponential probability distributions functions for the direct probabilities (the prior p(x) and the likelihood p(y\x)), for which the posterior p(x\y), and, consequently, the main posterior estimators are scale invariant. Among many properties, generalized exponentials can be considered as the maximum entropy probability distributions subject to the knowledge of a finite set of expectation values of some known functions.