Abstract: Deep neural networks are currently being used to produce good generative models for real world data. Such generative models had been successfully exploited to solve classical inverse problems like compressed sensing and super resolution, improving the state of art signal processing performance. In this talk we focus on the classical signal processing problem of image denoising. We analyze a simple toy model of feed-forward neural networks propose a theoretical setting that uses spherical harmonics to identify what mathematical properties of the activation functions will allow signal denoising with local methods. Ongoing work applies these ideas to stellar spectral modelling.
