Wedgelets: nearly minimax estimation of edges

Abstract

We study a simple \Horizon Model for the problem of recovering an image from noisy data; in this model the image has an edge with fi-Holder regularity. Adopting the viewpoint of computational harmonic analysis, we develop an overcomplete collection of atoms called wedgelets, dyadically organized indicator functions with a variety of locations, scales, and orientations. The wedgelet representation provides nearly-optimal representations of objects in the Horizon model, as measured by minimax description length. We show how to rapidly compute a wedgelet approximation to noisy data by flnding a special edgelet-decorated recursive partition which minimizes a complexity-penalized sum of squares. This estimate, using su-cient sub-pixel resolution, achieves nearly the minimax mean-squared error in the Horizon Model. In fact, the method is adaptive in the sense that it achieves nearly the minimax risk for any value of the unknown degree of regularity of the Horizon, 1• fi• 2. Wedgelet analysis and de-noising may be used successfully outside the Horizon model. We study images modelled as indicators of star-shaped sets with smooth bound- aries and show that complexity-penalized wedgelet partitioning achieves nearly the minimax risk in that setting also.