Shift invariant restoration - An overcomplete maxent MAP framework

Translation-invariant denoising was introduced by Coifman and Donoho to overcome Gibbs-type phenomena produced by transform-domain shrinkage estimators in the vicinity of signal discontinuities. Shrinkage estimators are in general not shift-invariant. Shift-invariant denoising consists of a simple averaging of the shrinkage estimates over a family of cyclic spatial-shifts of the image. Shift-invariant denoising is denoising in an overcomplete basis, and work in this area has been devoted towards finding a best basis in the overcomplete family. This paper presents a Maximum A Posteriori (MAP) framework for shift-invariant restoration of images using the maximum-entropy prior consistent with moment constraints on the transform coefficients in different subbands. The simple averaging of estimates in the classical shift-invariant denoising can then be shown to be a certain limiting case within this framework.