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Single Image De-Raining via Improved Generative Adversarial Nets.

Yi Ren¹, Shichao Li², Mengzhen Nie²

¹China Academy of Electronics and Information Technology (CAEIT), Beijing 100041, China.

Sensors (Basel, Switzerland)

|March 18, 2020

Summary

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This study introduces an improved generative adversarial network for single image de-raining. The method effectively locates, removes, and refines rain, enhancing image quality for analysis tasks.

Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Image quality degradation due to rain impacts visual analysis tasks like object detection.
Image de-raining is a significant research area addressing these challenges.

Purpose of the Study:

To propose an improved generative adversarial network (GAN) for single image de-raining.
To enhance the visual quality and analytical utility of images captured in rainy conditions.

Main Methods:

A divide-and-conquer strategy was employed, breaking down de-raining into rain locating, rain removing, and detail refining.
A multi-stream DenseNet (Rain Estimation Network) was developed for rain localization.
A GAN was utilized for rain streak removal.
A Refinement Network was implemented for detail restoration.

Keywords:

generative adversarial network image de-raining rain estimation refinement network

Related Experiment Videos

Main Results:

The proposed method successfully performed rain locating, removal, and detail refinement.
Experimental results on synthetic and real-world datasets demonstrated superior performance compared to existing methods.
Both objective and subjective evaluations confirmed the effectiveness of the proposed approach.

Conclusions:

The improved GAN-based method offers a robust solution for single image de-raining.
The divide-and-conquer approach effectively tackles the complexities of rain removal.
The method significantly enhances image quality, benefiting downstream computer vision tasks.