# Gradual Network for Single Image De-raining

arXiv: https://arxiv.org/abs/1909.09677 · HTML: https://mlsanity.com/paper/1909.09677/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Magic ELF: Image Deraining Meets Association Learning and Transformer](https://mlsanity.com/paper/2207.10455/) (2022)
- [Networks are Slacking Off: Understanding Generalization Problem in Image Deraining](https://mlsanity.com/paper/2305.15134/) (2023)
- [Snow Mask Guided Adaptive Residual Network for Image Snow Removal](https://mlsanity.com/paper/2207.04754/) (2022)
- [Revisiting the Generalization Problem of Low-level Vision Models Through the Lens of Image Deraining](https://mlsanity.com/paper/2502.12600/) (2025)
- [MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose](https://mlsanity.com/paper/2205.12583/) (2022)
- [DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View Representations for 3D Object Detection](https://mlsanity.com/paper/2405.10577/) (2024)
- [Direction-aware Feature-level Frequency Decomposition for Single Image Deraining](https://mlsanity.com/paper/2106.07941/) (2021)
- [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)
- [Dynamic Association Learning of Self-Attention and Convolution in Image Restoration](https://mlsanity.com/paper/2311.05147/) (2023)

## Abstract (arXiv)

Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel-wise regression directly. However, they are lacking in mining the balance between over-de-raining (e.g. removing texture details in rain-free regions) and under-de-raining (e.g. leaving rain streaks). In this paper, we firstly propose a coarse-to-fine network called Gradual Network (GraNet) consisting of coarse stage and fine stage for delving into single image de-raining with different granularities. Specifically, to reveal coarse-grained rain-streak characteristics (e.g. long and thick rain streaks/raindrops), we propose a coarse stage by utilizing local-global spatial dependencies via a local-global subnetwork composed of region-aware blocks. Taking the residual result (the coarse de-rained result) between the rainy image sample (i.e. the input data) and the output of coarse stage (i.e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e.g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block. Solid and comprehensive experiments on synthetic and real data demonstrate that our GraNet can significantly outperform the state-of-the-art methods by removing rain streaks with various densities, scales and shapes while keeping the image details of rain-free regions well-preserved.