Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation from noisy nonlinear measurements. We assume that the unknown signal lies near the range of a Lipschitz continuous generative model with bounded inputs. In particular, we consider two cases when the nonlinear link function is either unknown …