自然图像中的非局部自相似性已被证实是图像恢复的有效先验。然而，大多数现有的深度非局部方法为每个查询项分配固定数量的邻域块，忽略了非局部相关性的动态。此外，非局部相关性通常基于像素，容易因图像退化而产生偏差。为了纠正这些弱点，在本文中，我们提出了一种动态注意图学习模型（DAGL）来探索图像恢复块级别的动态非局部属性。具体来说，我们提出了一种改进的图模型，以对每个节点具有动态和自适应数量的邻居执行逐块图卷积。通过这种方式，图像内容可以通过其连接的邻域的数量自适应地平衡过度平滑和过度锐化的伪影，并且块方式的非局部相关性可以增强消息传递过程。各种图像恢复任务的实验结果：合成图像去噪、真实图像去噪、图像去马赛克和压缩伪影减少表明，我们的 DAGL 可以产生具有卓越精度和视觉质量的最新结果。
Rain removal plays an important role in the restoration of degraded images. Recently, data-driven methods have achieved remarkable success. However, these approaches neglect that the appearance of rain is often accompanied by low light conditions, which will further degrade the image quality. Therefore, it is very indispensable to jointly remove the rain and enhance the light for real-world rain image restoration. In this paper, we aim to address this problem from two aspects. First, we proposed a novel entangled network, namely EMNet, which can remove the rain and enhance illumination in one go. Specifically, two encoder-decoder networks interact complementary information through entanglement structure, and parallel rain removal and illumination enhancement. Considering that the encoder-decoder structure is unreliable in preserving spatial details, we employ a detail recovery network to restore the desired fine texture. Second, we present a new synthetic dataset, namely DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates the rainfall in the real world. EMNet is extensively evaluated on the proposed benchmark and achieves state-of-the-art results. In addition, after a simple transformation, our method outshines existing methods in both rain removal and low-light image enhancement. The source code and dataset will be made publicly available later.