{"created":"2023-05-15T12:35:55.464293+00:00","id":12100,"links":{},"metadata":{"_buckets":{"deposit":"b9d53039-52cc-46c7-8bd5-7ba6b50f3e5e"},"_deposit":{"created_by":4,"id":"12100","owners":[4],"pid":{"revision_id":0,"type":"depid","value":"12100"},"status":"published"},"_oai":{"id":"oai:kait.repo.nii.ac.jp:00012100","sets":["2:16:43:192"]},"author_link":[],"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-03-01","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"42","bibliographicPageStart":"37","bibliographicVolumeNumber":"44","bibliographic_titles":[{"bibliographic_title":"神奈川工科大学研究報告.B,理工学編"}]}]},"item_10002_description_19":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"As a method of estimating Gaussian noise superimposed on the image, there is an estimation method based on MAD. The method based on MAD has good estimation accuracy for images with many flat area. However, the estimation accuracy is not good for images with many edges and detail signals. We proposed the method to extend the method based on MAD to correct the Gaussian noise estimate according to the type of image. As a result, it was possible to improve the estimation accuracy even in an image including many edges and detail signals. However, improvement in estimation accuracy is effective only when the Gaussian noise is large, and a very effective result cannot be obtained when the Gaussian noise is small. In this paper, we propose the method for improving estimation accuracy for images with small Gaussian noise and many edges and detail signals. In the proposed method, an estimation method that focuses on the additiveness of the Gaussian distribution is applied only to images that contain many edges and detail signals. The proposed method improved the noise estimation accuracy by about 27% compared to the conventional method.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10002_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.34411/00032028","subitem_identifier_reg_type":"JaLC"}]},"item_10002_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"神奈川工科大学"}]},"item_10002_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12669200","subitem_source_identifier_type":"NCID"}]},"item_10002_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"21882878","subitem_source_identifier_type":"PISSN"}]},"item_10002_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 貴士","creatorNameLang":"ja"},{"creatorName":"Suzuki, Takashi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"長沼, 一輝","creatorNameLang":"ja"},{"creatorName":"Naganuma, Kazuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"辻, 裕之","creatorNameLang":"ja"},{"creatorName":"Tsuji, Hiroyuki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"木村, 誠聡","creatorNameLang":"ja"},{"creatorName":"Kimura, Tomoaki","creatorNameLang":"en"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-05-11"}],"displaytype":"detail","filename":"kkb-044-006.pdf","filesize":[{"value":"2.4 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"kkb-044-006.pdf","objectType":"fulltext","url":"https://kait.repo.nii.ac.jp/record/12100/files/kkb-044-006.pdf"},"version_id":"600f4bfc-446d-4f39-8f8e-269f4f5ad987"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Gaussian Noise","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Standard Deviation","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Estimate","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"Gaussian Distribution","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ガウス分布の加法性に基づいたガウス雑音の標準偏差の推定法","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ガウス分布の加法性に基づいたガウス雑音の標準偏差の推定法","subitem_title_language":"ja"},{"subitem_title":"An Estimate the Standard Deviation of Gaussian Noise Based on the Additiveness of Gaussian Distribution","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"4","path":["192"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-05-11"},"publish_date":"2021-05-11","publish_status":"0","recid":"12100","relation_version_is_last":true,"title":["ガウス分布の加法性に基づいたガウス雑音の標準偏差の推定法"],"weko_creator_id":"4","weko_shared_id":-1},"updated":"2025-07-04T06:18:39.819876+00:00"}