ログイン 新規登録
言語:

WEKO3

  • トップ
  • ランキング


インデックスリンク

インデックスツリー

  • RootNode

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

  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 Degraded Image Super-Resolution with Joint De-artifact and Deblurring

https://ipsj.ixsq.nii.ac.jp/records/203413
https://ipsj.ixsq.nii.ac.jp/records/203413
6a84c278-0743-4428-9471-cc88b1ffdb4a
名前 / ファイル ライセンス アクション
IPSJ-AVM20108011.pdf IPSJ-AVM20108011.pdf (2.1 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 Degraded Image Super-Resolution with Joint De-artifact and Deblurring
タイトル
言語 en
タイトル A Study on Degraded Image Super-Resolution with Joint De-artifact and 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

Search repository
Kazuyuki, Tasaka

× Kazuyuki, Tasaka

Kazuyuki, Tasaka

Search repository
著者名(英) Jianfeng, Xu

× Jianfeng, Xu

en Jianfeng, Xu

Search repository
Kazuyuki, Tasaka

× Kazuyuki, Tasaka

en Kazuyuki, Tasaka

Search repository
論文抄録
内容記述タイプ Other
内容記述 In this paper, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. An all-in-one collaboration framework (ACF) with a learnable junction unit has been proposed to handle two major problems that exist in MTL: How to share and How much to share. Specifically, considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur (dynamic blur), and spatial structure information of the input image in parallel under a three-branch architecture. Then, based on the feedback from the final super-resolution reconstruction, we train a learnable junction unit with a dual-voting mechanism to selectively filter or preserve these shared feature representations. This unit facilitates collaboration between multi-tasks and learns an optimal combination of different features. Experimental results show that the proposed all-in-one collaboration framework not only produce favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods.
論文抄録(英)
内容記述タイプ Other
内容記述 In this paper, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. An all-in-one collaboration framework (ACF) with a learnable junction unit has been proposed to handle two major problems that exist in MTL: How to share and How much to share. Specifically, considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur (dynamic blur), and spatial structure information of the input image in parallel under a three-branch architecture. Then, based on the feedback from the final super-resolution reconstruction, we train a learnable junction unit with a dual-voting mechanism to selectively filter or preserve these shared feature representations. This unit facilitates collaboration between multi-tasks and learns an optimal combination of different features. Experimental results show that the proposed all-in-one collaboration framework not only produce favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods.
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 11, 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
戻る
0
views
See details
Views

Versions

Ver.1 2025-01-19 20:33:18.000290
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

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

Loading...

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3