  In this paper, we propose a new control framework called the moving endpoint
control to restore images corrupted by different degradation levels in one
model. The proposed control problem contains a restoration dynamics which is
modeled by an RNN. The moving endpoint, which is essentially the terminal time
of the associated dynamics, is determined by a policy network. We call the
proposed model the dynamically unfolding recurrent restorer (DURR). Numerical
experiments show that DURR is able to achieve state-of-the-art performances on
blind image denoising and JPEG image deblocking. Furthermore, DURR can well
generalize to images with higher degradation levels that are not included in
the training stage.
