We extend the hierarchical decomposition of an image as a sum of constituents of different scales, introduced by Tadmor, Nezzar and Vese in 2004, to a general setting. We develop a theory for multiscale decompositions which, besides extending the one of Tadmor, Nezzar and Vese to arbitrary L^2 functions, is applicable to nonlinear inverse problems, as well as to other imaging problems. As a significant example, we present applications to the inverse conductivity problem. This is a joint work with Klas Modin and Adrian Nachman.