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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/16713
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| タイトル: | Attention-model Guided Image Enhancement for Robotic Vision Applications |
| 著者: | Yi, Ming
Li, Wanxiang
Elibol, Armagan
Chong, Nak Young |
| 発行日: | 2020-06 |
| 出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
| 誌名: | Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR) |
| 開始ページ: | 514 |
| 終了ページ: | 519 |
| DOI: | 10.1109/UR49135.2020.9144966 |
| 抄録: | Optical data is one of the crucial information resources for robotic platforms to sense and interact with the environment being employed. Obtained image quality is the main factor of having a successful application of sophisticated methods (e.g., object detection and recognition). In this paper, a method is proposed to improve the image quality by enhancing the lighting and denoising. The proposed method is based on a generative adversarial network (GAN) structure. It makes use of the attention model both to guide the enhancement process and to apply denoising simultaneously thanks to the step of adding noise on the input of discriminator networks. Detailed experimental and comparative results using real datasets were presented in order to underline the performance of the proposed method. |
| Rights: | This is the author's version of the work. Copyright (C) 2020 IEEE. Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR), 2020, pp.514-519. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/16713 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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このアイテムのファイル:
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| C2_UR20_0043_FI.pdf | | 800Kb | Adobe PDF | 見る/開く |
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