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タイトル
  • 機械学習によるRAW現像技術の開発
その他のタイトル
  • Development of RAW Image Format Converter Using Deep Learning
作成者
    • 伊東, 直毅
    • 甲斐, 宗徳
主題
  • Other RAW Development
  • Other Deep Learning
  • Other Machine Learning
  • Other Parallel Processing
  • Other GPGPU
内容注記
  • Other type:Article
  • Other RAW development is an operation manually performed by a person in order to finish a photograph according to his / her desire. At this time, many image adjustment parameters are set, and confirmation and adjustment are repeated. The work requires much time and effort, and it is very difficult to make many photos taken into the desired images. However, if deep learning is used, learning RAW images and photographs after RAW development may create photographs of the same level as manual RAW development. In this research, we succeeded in developing a machine learning model that performs RAW development using RAW data as input. We also aimed to improve the processing speed of learning using GPGPU. As a result of comparing processing in which RAW development is performed for 100 images with a single multi-core CPU using a parallel program and processing using a GPGPU, it is shown that the latter can be significantly faster.
  • Other identifier:http://repository.seikei.ac.jp/dspace/handle/10928/1175
出版者 成蹊大学理工学部
日付
    Issued2019-06-01
言語
  • jpn
資源タイプ departmental bulletin paper
出版タイプ VoR
資源識別子 URI http://hdl.handle.net/10928/1175 , DOI https://doi.org/10.15018/00000979
ID
  • JaLC 10.15018/00000979
収録誌情報
    • NCID AA1203510X
    • ISSN 1880-2265
      • 成蹊大学理工学研究報告 = The journal of the Faculty of Science and Technology, Seikei University
      • 巻56 号1 開始ページ9 終了ページ14
ファイル
コンテンツ更新日時 2023-06-26