# Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2211.05234 · HTML: https://mlsanity.com/paper/2211.05234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 1 Nov 2022.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [I Can See Clearly Now : Image Restoration via De-Raining](https://mlsanity.com/paper/1901.00893/) (2019)
- [On the Evaluation of Conditional GANs](https://mlsanity.com/paper/1907.08175/) (2019)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [Applying Deep Learning to Specific Learning Disorder Screening](https://mlsanity.com/paper/2008.13525/) (2020)
- [Generating Photo-realistic Images from LiDAR Point Clouds with Generative Adversarial Networks](https://mlsanity.com/paper/2112.11245/) (2021)

## Abstract (arXiv)

We presented a method for improving computer vision tasks on images affected by adverse weather conditions, including distortions caused by adherent raindrops. Overcoming the challenge of applying computer vision to images affected by adverse weather conditions is essential for autonomous vehicles utilizing RGB cameras. For this purpose, we trained an appropriate generative adversarial network and showed that it was effective at removing the effect of the distortions, in the context of image reconstruction and computer vision tasks. We showed that object recognition, a vital task for autonomous driving vehicles, is completely impaired by the distortions and occlusions caused by adherent raindrops and that performance can be restored by our de-raining model. The approach described in this paper could be applied to all adverse weather conditions.