  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.
