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首页> 外文会议>IEEE International Conference on Image Processing >R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery
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R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery

机译：R3L：通过剩余恢复将深度增强学习用于复发性神经网络，通过剩余恢复进行图像去噪

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摘要

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.
机译：最先进的图像Denoisers通过确定性培训利用各种类型的深神经网络。或者，最近的作品利用了深度加强学习，以便在具有多样化或未知的损坏中恢复图像。虽然深度加强学习可以在图像恢复中生成用于操作员选择或架构搜索的有效策略网络，但如何将其连接到求解逆问题的经典确定性训练仍不清楚。在这项工作中，我们通过使用加强学习，称为R3L，提出了一种通过剩余恢复的新型图像去噪方案。我们表明R3L等同于使用随机奖励训练的深度复发性神经网络，与许多流行的欺诈者使用监督学习具有确定性损失。为了基准R3L中加固学习的有效性，我们用相同的架构训练一种经常性的神经网络，使用确定性损失进行残余恢复，从而分析两种不同的训练策略如何影响去噪性能。利用这种统一的基准系统，我们证明，当估计噪声水平变化时，所提出的R3L在图像去噪中具有更好的相互平衡和鲁棒性，与使用确定性训练的对应力以及各种最先进的图像去噪算法相比。

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