{"created":"2025-01-19T01:01:59.706801+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197557","sets":["1164:3616:9728:9821"]},"path":["9821"],"owner":"44499","recid":"197557","title":["スパース・低ランクモデリングに基づく画像処理"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-06-06"},"_buckets":{"deposit":"55a2096b-cdc8-4814-9bd6-ea24c40202ea"},"_deposit":{"id":"197557","pid":{"type":"depid","value":"197557","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"スパース・低ランクモデリングに基づく画像処理","author_link":["474153","474152"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"スパース・低ランクモデリングに基づく画像処理"},{"subitem_title":"Image Processing Based on Sparse and Low-rank Modeling","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"招待講演","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-06-06","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"北九州市立大学国際環境工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Environmental Engineering, The University of Kitakyushu","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/197557/files/IPSJ-AVM19105010.pdf","label":"IPSJ-AVM19105010.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AVM19105010.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"27"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"465a5085-381e-4c98-92b5-cd187f4fb620","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"京地, 清介"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Seisuke, Kyochi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438399","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8582","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，近年盛んに研究されている凸最適化を用いた画像復元について，その基本事項と著者グループの関連研究をいくつか紹介する．カメラで取得された画像には，その取得過程で発生するノイズ，焦点ぼけ，手ぶれなど様々な要因によりしばしば劣化が生じる．画像復元ではこれらの劣化が生じる過程を数理的にモデル化し，逆問題を解くことによって所望画像を推定する．高精度な画像復元を行うためには，所望画像が満たすべき性質を反映した正則化関数を適切に設計し，最適化問題を構成する目的関数に導入することが必要となる．本稿では，凸最適化の中でも特にスパース ・低ランクモデリングの観点に基づく正則化関数の設計例を示し，実験にて有効性を確認する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper presents fundamental tools for image recovery by convex optimization and introduces some case study from the author's related works. During the image acquisition, images are often corrupted by many kinds of degradation, such as noise, blur caused by incorrect focus or handshaking. In image recovery framework, each process of degradation is mathematically modeled as a regularizer and integrated into the cost function. Finally, by solving the inverse problem, the desired image is estimated. For accurate image recovery, a suitable regularizer should be designed. In this paper, some convex regularizers based on sparsity and low-rankness are presented and shown their effectiveness in experiments.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告オーディオビジュアル複合情報処理（AVM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-06-06","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2019-AVM-105"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":197557,"updated":"2025-01-19T22:18:29.015058+00:00","links":{}}