  To estimate geometrically regular images in the white noise model and obtain
an adaptive near asymptotic minimaxity result, we consider a model selection
based bandlet estimator. This bandlet estimator combines the best basis
selection behaviour of the model selection and the approximation properties of
the bandlet dictionary. We derive its near asymptotic minimaxity for
geometrically regular images as an example of model selection with general
dictionary of orthogonal bases. This paper is thus also a self contained
tutorial on model selection with orthogonal bases dictionary.
