# DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks

arXiv: https://arxiv.org/abs/1908.10267 · HTML: https://mlsanity.com/paper/1908.10267/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [Unpaired Deep Image Deraining Using Dual Contrastive Learning](https://mlsanity.com/paper/2109.02973/) (2021)
- [Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness](https://mlsanity.com/paper/2104.13743/) (2021)
- [Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework](https://mlsanity.com/paper/2103.12318/) (2021)
- [TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning](https://mlsanity.com/paper/2209.01373/) (2022)
- [Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM](https://mlsanity.com/paper/2111.03615/) (2021)
- [A comprehensive benchmark analysis for sand dust image reconstruction](https://mlsanity.com/paper/2202.03031/) (2022)
- [ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images](https://mlsanity.com/paper/2203.09275/) (2022)
- [Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning](https://mlsanity.com/paper/2204.02772/) (2022)
- [Perceptive self-supervised learning network for noisy image watermark removal](https://mlsanity.com/paper/2403.02211/) (2024)
- [A self-supervised CNN for image watermark removal](https://mlsanity.com/paper/2403.05807/) (2024)
- [Towards an Effective and Efficient Transformer for Rain-by-snow Weather Removal](https://mlsanity.com/paper/2304.02860/) (2023)
- [When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters](https://mlsanity.com/paper/2203.00258/) (2022)
- [Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information](https://mlsanity.com/paper/2212.02198/) (2022)
- [Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal](https://mlsanity.com/paper/2503.16165/) (2025)
- [SSH-Net: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal](https://mlsanity.com/paper/2505.05088/) (2025)

## Abstract (arXiv)

Image deraining is a fundamental, yet not well-solved problem in computer vision and graphics. The traditional image deraining approaches commonly behave ineffectively in medium and heavy rain removal, while the learning-based ones lead to image degradations such as the loss of image details, halo artifacts and/or color distortion. Unlike existing image deraining approaches that lack the detail-recovery mechanism, we propose an end-to-end detail-recovery image deraining network (termed a DRD-Net) for single images. We for the first time introduce two sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. We have three key contributions. First, we present a rain residual network to remove rain streaks from the rainy images, which combines the squeeze-and-excitation (SE) operation with residual blocks to make full advantage of spatial contextual information. Second, we design a new connection style block, named structure detail context aggregation block (SDCAB), which aggregates context feature information and has a large reception field. Third, benefiting from the SDCAB, we construct a detail repair network to encourage the lost details to return for eliminating image degradations. We have validated our approach on four recognized datasets (three synthetic and one real-world). Both quantitative and qualitative comparisons show that our approach outperforms the state-of-the-art deraining methods in terms of the deraining robustness and detail accuracy. The source code has been available for public evaluation and use on GitHub.