# Residual-Guide Feature Fusion Network for Single Image Deraining

arXiv: https://arxiv.org/abs/1804.07493 · HTML: https://mlsanity.com/paper/1804.07493/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

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

- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [DRT: A Lightweight Single Image Deraining Recursive Transformer](https://mlsanity.com/paper/2204.11385/) (2022)
- [Visible Watermark Removal via Self-calibrated Localization and Background Refinement](https://mlsanity.com/paper/2108.03581/) (2021)
- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [From heavy rain removal to detail restoration: A faster and better network](https://mlsanity.com/paper/2205.03553/) (2022)
- [Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity](https://mlsanity.com/paper/2211.00837/) (2022)
- [Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM](https://mlsanity.com/paper/2111.03615/) (2021)
- [Non-local Channel Aggregation Network for Single Image Rain Removal](https://mlsanity.com/paper/2103.02488/) (2021)
- [Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2104.12100/) (2021)
- [Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal](https://mlsanity.com/paper/2204.13420/) (2022)
- [Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal](https://mlsanity.com/paper/2206.06803/) (2022)
- [Potential Auto-driving Threat: Universal Rain-removal Attack](https://mlsanity.com/paper/2211.09959/) (2022)
- [Uncertainty-Driven Multi-Scale Feature Fusion Network for Real-time Image Deraining](https://mlsanity.com/paper/2307.09728/) (2023)
- [A Prototype Unit for Image De-raining using Time-Lapse Data](https://mlsanity.com/paper/2412.19459/) (2024)
- [Single Image Rain Streak Removal Using Harris Corner Loss and R-CBAM Network](https://mlsanity.com/paper/2507.23185/) (2025)

## Abstract (arXiv)

Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems. Deep learning based methods have found great success in image deraining tasks. In this paper, we propose a novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts highquality reconstruction. Specifically, we propose a cascaded network and adopt residuals generated from shallower blocks to guide deeper blocks. By using this strategy, we can obtain a coarse to fine estimation of negative residual as the blocks go deeper. The outputs of different blocks are merged into the final reconstruction. We adopt recursive convolution to build each block and apply supervision to all intermediate results, which enable our model to achieve promising performance on synthetic and real-world data while using fewer parameters than previous required. ResGuideNet is detachable to meet different rainy conditions. For images with light rain streaks and limited computational resource at test time, we can obtain a decent performance even with several building blocks. Experiments validate that ResGuideNet can benefit other low- and high-level vision tasks.