  State-of-the-art image denoisers exploit various types of deep neural
networks via deterministic training. Alternatively, very recent works utilize
deep reinforcement learning for restoring images with diverse or unknown
corruptions. Though deep reinforcement learning can generate effective policy
networks for operator selection or architecture search in image restoration,
how it is connected to the classic deterministic training in solving inverse
problems remains unclear. In this work, we propose a novel image denoising
scheme via Residual Recovery using Reinforcement Learning, dubbed R3L. We show
that R3L is equivalent to a deep recurrent neural network that is trained using
a stochastic reward, in contrast to many popular denoisers using supervised
learning with deterministic losses. To benchmark the effectiveness of
reinforcement learning in R3L, we train a recurrent neural network with the
same architecture for residual recovery using the deterministic loss, thus to
analyze how the two different training strategies affect the denoising
performance. With such a unified benchmarking system, we demonstrate that the
proposed R3L has better generalizability and robustness in image denoising when
the estimated noise level varies, comparing to its counterparts using
deterministic training, as well as various state-of-the-art image denoising
algorithms.
