Removing Raindrops from Nighttime Vehicle Onboard Images Using Generative Adversarial Networks with Ushaped Transformer Structure Generators and a Method for Generating Nighttime Raindrop Images for Training
U型Transformer構造の生成器を持つGANによる夜間雨滴画像からの雨滴除去手法と学習用夜間雨滴画像の生成法の提案
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- Publication code
- 20244059
- Paper/Info type
- JSAE Transaction
Vol.55 No.1
- Pages
- 172-179(Total 8 p)
- Date of publication
- Jan 2024
- Publisher
- JSAE
- Language
- Japanese
Detailed Information
| Category(J) | 論文 Translation |
|---|---|
| Category(E) | Paper |
| Author(J) | 1) 田中 良弥, 2) 中村 恭之 |
| Author(E) | 1) Ryoya Tanaka, 2) Takayuki Nakamura |
| Affiliation(J) | 1) 和歌山大学, 2) 和歌山大学 |
| Abstract(J) | 本論文では，セマンティックセグメンテーション手法やCG分野で利用されている手法を用いて，雨滴が付着していない昼間画像から夜間雨滴画像を作成する手法を提案する．また夜間雨天時に車載カメラから取得した雨滴が付着した画像から雨滴除去を行う深層学習法を提案する．提案法の有効性を確認するために実験結果を示す． Translation |
| Abstract(E) | In this paper, we propose a method for raindrop removal from rain-drop coated images acquired during nighttime rainy weather. To the best of our knowledge, no previous deep learning-based method for raindrop removal has been developed for nighttime images. This is because it is difficult to identify raindrop regions in images acquired at night, making it difficult to remove raindrops, and there is no nighttime raindrop image dataset to train a deep learning neural network.To solve these problems, we propose a method to create nighttime raindrop images from daytime images without raindrops by using a deep learning based semantic segmentation method and a method used in the field of computer graphics. In order to improve the ability to identify raindrop regions, we propose a GAN architecture that incorporates the U-shaped Transformer structure into the GAN generator. In addition, we propose to apply histogram flattening to the input images as a preprocessing step during training so that the GAN can be trained stably on a nighttime raindrop image dataset. Experimental results are presented to verify the effectiveness of the proposed method. |
