  We study image inverse problems with a normalizing flow prior. Our
formulation views the solution as the maximum a posteriori estimate of the
image conditioned on the measurements. This formulation allows us to use noise
models with arbitrary dependencies as well as non-linear forward operators. We
empirically validate the efficacy of our method on various inverse problems,
including compressed sensing with quantized measurements and denoising with
highly structured noise patterns. We also present initial theoretical recovery
guarantees for solving inverse problems with a flow prior.
