通过深度强化学习构建图像复原工具链
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
摘要：
本文提出了一种基于强化学习的图像复原新方法。与现有的研究主要是针对特定任务训练单个大型网络不同，我们准备了一个工具箱，由不同复杂度的小规模卷积网络组成，专门用于不同的任务。我们的方法，RL-Restore，然后学习一个策略，从工具箱中选择适当的工具，逐步恢复损坏的图像的质量。我们制定了一个逐步的奖励函数成比例的图像是如何恢复在每一步学习的行动政策。我们还设计了一个联合学习计划，训练代理和工具，以更好地表现在处理不确定性。与传统的人工设计的网络相比，RL-Restore能够使用动态形成的工具链以更有效的参数方式恢复被复杂和未知失真破坏的图像。
英文摘要：
We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different complexities and specialized in different tasks. Our method, RL-Restore, then learns a policy to select appropriate tools from the toolbox to progressively restore the quality of a corrupted image. We formulate a step-wise reward function proportional to how well the image is restored at each step to learn the action policy. We also devise a joint learning scheme to train the agent and tools for better performance in handling uncertainty. In comparison to conventional human-designed networks, RL-Restore is capable of restoring images corrupted with complex and unknown distortions in a more parameter-efficient manner using the dynamically formed toolchain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
