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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16713

Title: Attention-model Guided Image Enhancement for Robotic Vision Applications
Authors: Yi, Ming
Li, Wanxiang
Elibol, Armagan
Chong, Nak Young
Issue Date: 2020-06
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR)
Start page: 514
End page: 519
DOI: 10.1109/UR49135.2020.9144966
Abstract: 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
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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