Multi-scale Attentive Image De-raining Networks via Neural Architecture Search

TL;DR

This paper introduces a neural architecture search framework for multi-scale attentive image de-raining, automating the design of effective networks and improving performance on synthetic and real rainy images.

Contribution

The proposed MANAS framework automatically searches for multi-scale attentive architectures, reducing manual design effort and enhancing de-raining performance with a novel search space and training strategy.

Findings

Outperforms existing methods on synthetic and real rainy images

Improves object detection and segmentation in rainy conditions

Achieves robust de-raining with controllable model complexity

Abstract

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.