Multi-scale architectures and attention modules have shown effectiveness in many deep learning-based image de-raining methods. However, manually designing and integrating these two components into a neural network requires a bulk of labor and extensive expertise. In this article, a high-performance multi-scale attentive neural architecture search (MANAS) framework is technically developed for image deraining. The proposed method formulates a new multi-scale attention search space with multiple flexible modules that are favorite to the image de-raining task. Under the search space, multi-scale attentive cells are built, which are further used to construct a powerful image de-raining network. The internal multiscale attentive architecture of the de-raining network is searched automatically through a gradient-based search algorithm, which avoids the daunting procedure of the manual design to some extent. Moreover, in order to obtain a robust image de-raining model, a practical and effective multi-to-one training strategy is also presented to allow the de-raining network to get sufficient background information from multiple rainy images with the same background scene, and meanwhile, multiple loss functions including external loss, internal loss, architecture regularization loss, and model complexity loss are jointly optimized to achieve robust de-raining performance and controllable model complexity. Extensive experimental results on both synthetic and realistic rainy images, as well as the down-stream vision applications (i.e., objection detection and segmentation) consistently demonstrate the superiority of our proposed method. The code is publicly available at https://github.com/lcai-gz/MANAS.

翻译：多尺度架构和注意力模块在许多基于深度学习的图像去雨方法中展现出有效性。然而，手动设计并将这两个组件集成到神经网络中需要大量人力和专业知识。本文技术性地开发了一个高性能多尺度注意力神经架构搜索（MANAS）框架用于图像去雨。该方法构建了一个新的多尺度注意力搜索空间，其中包含多个对图像去雨任务有利的灵活模块。在该搜索空间下，构建了多尺度注意力单元，并进一步用于构建强大的图像去雨网络。去雨网络内部的多尺度注意力架构通过基于梯度的搜索算法自动搜索，这在一定程度上避免了繁琐的手动设计过程。此外，为了获得鲁棒的图像去雨模型，还提出了一种实用有效的多对一训练策略，允许去雨网络从具有相同背景场景的多张雨图中获取充足的背景信息，同时联合优化包括外部损失、内部损失、架构正则化损失和模型复杂度损失在内的多个损失函数，以实现鲁棒的去雨性能和可控的模型复杂度。在合成雨图和真实雨图以及下游视觉应用（即目标检测和分割）上的大量实验结果一致证明了我们提出的方法的优越性。代码已在 https://github.com/lcai-gz/MANAS 公开。