R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery

Abstract

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-ofthe-art image denoising algorithms.

ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement

“…While these methods focus on global image restoration, Furuta et al [3] proposes pixelRL to enable pixel-wise image restoration which is more flexible. More recently, Zhang et al [26] proposes R3L, which applies DRL to pixel-wise image denoising via direct residual recovery. However, the aforementioned methods all require the external set of "highquality" training images, which can be highly limited in practice.…”