Title: Empirical Bayes least squares estimation without an explicit prior (NYU-CS-TR900) Authors: Martin Raphan and Eero P. Simoncelli Abstract: Bayesian estimators are commonly constructed using an explicit prior model. In many applications, one does not have such a model, and it is difficult to learn since one does not have access to uncorrupted measurements of the variable being estimated. In many cases however, including the case of contamination with additive Gaussian noise, the Bayesian least squares estimator can be formulated directly in terms of the distribution of noisy measurements. We demonstrate the use of this formulation in removing noise from photographic images. We use a local approximation of the noisy measurement distribution by exponentials over adaptively chosen intervals, and derive an estimator from this approximate distribution. We demonstrate through simulations that this adaptive Bayesian estimator performs as well or better than previously published estimators based on simple prior models.