The images distortion leads to the loss of image information and the degradation of perceptual quality. To solve this problem, we investigate a novel mixed distortion image enhancement method based on the parallel network combines deep residual and reinforcement learning. The no-reference image quality assessment algorithm is used to determine the type and level of distorted images accurately. According to the type of distortion, the mixed distortion images enter one of the subsequent parallel joint learning networks automatically. In the joint learning framework, we prepare different residual networks to handle specialized restoration assignments including deblurring, denoising, or JPEG compression. Simultaneously, reinforcement learning then learns a policy to select the next best restoration tasks to progressively restore the quality of a corrupted image. Our method is capable of restoring images corrupted with complex mixed distortions in a more parameter-efficient manner in comparison to conventional networks. The extensive experiments on synthetic and real-world images validate the superior performances of the proposed method.

中文翻译：

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基于深度残差学习和强化学习联合的混合失真图像增强方法

图像失真导致图像信息的丢失和感知质量的下降。为了解决这个问题，我们研究了一种基于并行网络结合深度残差和强化学习的新型混合失真图像增强方法。无参考图像质量评估算法用于准确判断失真图像的类型和程度。根据失真的类型，混合失真图像自动进入后续并行联合学习网络之一。在联合学习框架中，我们准备了不同的残差网络来处理专门的恢复任务，包括去模糊、去噪或 JPEG 压缩。同时地，强化学习然后学习一种策略来选择下一个最佳恢复任务，以逐步恢复损坏图像的质量。与传统网络相比，我们的方法能够以更参数有效的方式恢复因复杂混合失真而损坏的图像。对合成图像和真实世界图像的大量实验验证了所提出方法的优越性能。