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  1. 研究報告
  2. オーディオビジュアル複合情報処理(AVM)
  3. 2020
  4. 2020-AVM-108

A Study on Motion-robust Video Deblurring

https://ipsj.ixsq.nii.ac.jp/records/203414
https://ipsj.ixsq.nii.ac.jp/records/203414
a27465b7-b15b-4b87-8078-8d7ff159ed3f
名前 / ファイル ライセンス アクション
IPSJ-AVM20108012.pdf IPSJ-AVM20108012.pdf (6.6 MB)
Copyright (c) 2020 by the Information Processing Society of Japan
オープンアクセス
Item type SIG Technical Reports(1)
公開日 2020-02-20
タイトル
タイトル A Study on Motion-robust Video Deblurring
タイトル
言語 en
タイトル A Study on Motion-robust Video Deblurring
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
KDDI Research, Inc.
著者所属
KDDI Research, Inc.
著者所属(英)
en
KDDI Research, Inc.
著者所属(英)
en
KDDI Research, Inc.
著者名 Jianfeng, Xu

× Jianfeng, Xu

Jianfeng, Xu

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Kazuyuki, Tasaka

× Kazuyuki, Tasaka

Kazuyuki, Tasaka

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著者名(英) Jianfeng, Xu

× Jianfeng, Xu

en Jianfeng, Xu

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Kazuyuki, Tasaka

× Kazuyuki, Tasaka

en Kazuyuki, Tasaka

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論文抄録
内容記述タイプ Other
内容記述 Most existing video deblurring works focus on the use of temporal redundancy and lack utilization of the prior information about data itself, resulting in strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened MOTION-robust video deblurring model (PROMOTION) suitable for both global and local blurry scenarios. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information (including illumination, structure, and motion priors), which enhances the model's blur perception while mitigating the output's artifacts. On the other hand, we design the priors representing blur distribution, which enables our model to better handle non-uniform blur in spatio-temporal domain. In addition to the classical camera shake caused blurry scenes, we also prove the generalization of the model for local blur in real scenario, resulting in better accuracy of hand pose estimation.
論文抄録(英)
内容記述タイプ Other
内容記述 Most existing video deblurring works focus on the use of temporal redundancy and lack utilization of the prior information about data itself, resulting in strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened MOTION-robust video deblurring model (PROMOTION) suitable for both global and local blurry scenarios. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information (including illumination, structure, and motion priors), which enhances the model's blur perception while mitigating the output's artifacts. On the other hand, we design the priors representing blur distribution, which enables our model to better handle non-uniform blur in spatio-temporal domain. In addition to the classical camera shake caused blurry scenes, we also prove the generalization of the model for local blur in real scenario, resulting in better accuracy of hand pose estimation.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN10438399
書誌情報 研究報告オーディオビジュアル複合情報処理(AVM)

巻 2020-AVM-108, 号 12, p. 1-6, 発行日 2020-02-20
ISSN
収録物識別子タイプ ISSN
収録物識別子 2188-8582
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
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Cite as

Jianfeng, Xu, Kazuyuki, Tasaka, 2020: 情報処理学会, 1–6 p.

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