  Low-rank Deconvolution (LRD) has appeared as a new multi-dimensional
representation model that enjoys important efficiency and flexibility
properties. In this work we ask ourselves if this analytical model can compete
against Deep Learning (DL) frameworks like Deep Image Prior (DIP) or Blind-Spot
Networks (BSN) and other classical methods in the task of signal restoration.
More specifically, we propose to extend LRD with differential regularization.
This approach allows us to easily incorporate Total Variation (TV) and integral
priors to the formulation leading to considerable performance tested on signal
restoration tasks such image denoising and video enhancement, and at the same
time benefiting from its small computational cost.
