Summary of Leveraging Scene Geometry and Depth Information For Robust Image Deraining, by Ningning Xu and Jidong J. Yang
Leveraging Scene Geometry and Depth Information for Robust Image Deraining
by Ningning Xu, Jidong J. Yang
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel learning framework for image deraining, which is crucial for enhancing the vision of autonomous vehicles in rainy conditions. Unlike previous works that employ a single network architecture, this approach integrates multiple networks to effectively capture the underlying scene structure and produce clearer images. The framework consists of an AutoEncoder for deraining, an auxiliary network to incorporate depth information, and two supervision networks to enforce feature consistency between rainy and clear scenes. This multi-network design enables the model to generate more accurately derained images, leading to improved object detection for autonomous vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make images clearer in the rain so that self-driving cars can see better. Right now, most methods don’t use all the information they could be using from the scene, like depth information. The authors came up with a new way to do image deraining by combining multiple networks together. This helps them get more accurate results and even improves object detection in images. |
Keywords
» Artificial intelligence » Autoencoder » Object detection
