# Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

arXiv: https://arxiv.org/abs/2209.07808 · HTML: https://mlsanity.com/paper/2209.07808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Clearing the Skies: A deep network architecture for single-image rain removal](https://mlsanity.com/paper/1609.02087/) (2016)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [Lightweight Pyramid Networks for Image Deraining](https://mlsanity.com/paper/1805.06173/) (2018)
- [Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids](https://mlsanity.com/paper/2111.05700/) (2021)

## Built on by (most cited first, 1 of 1)

- [Enhanced Edge-Perceptual Guided Image Filtering](https://mlsanity.com/paper/2310.10387/) (2023)

## Abstract (arXiv)

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.