  It has been experimentally established that deep neural networks can be used
to produce good generative models for real world data. It has also been
established that such generative models can be exploited to solve classical
inverse problems like compressed sensing and super resolution. In this work we
focus on the classical signal processing problem of image denoising. We propose
a theoretical setting that uses spherical harmonics to identify what
mathematical properties of the activation functions will allow signal denoising
with local methods.
