Learned Proximal Networks for Unsupervised Inverse Problem Solving with Interpretable Priors

Learned proximal networks (LPNs) are a new class of deep neural networks that exactly implement the proximal operator of a general learned function, enabling the recovery of the underlying data distribution's log-prior in an unsupervised manner. LPNs can be used to solve general inverse problems with convergence guarantees.