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Wiener-Based Inpainting Quality Prediction

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この文献へのリンクには次のURLを使用してください:http://hdl.handle.net/2115/67806

タイトル: Wiener-Based Inpainting Quality Prediction
著者: OGAWA, Takahiro 著作を一覧する
TANAKA, Akira 著作を一覧する
HASEYAMA, Miki 著作を一覧する
キーワード: quality prediction
inpainting
Wiener filter
least-squares estimation
発行日: 2017年10月 1日
出版者: Institute of Electronics, Information and Communication Engineers
誌名: IEICE Transactions on Information and Systems
巻: E100.D
号: 10
開始ページ: 2614
終了ページ: 2626
出版社 DOI: 10.1587/transinf.2017EDP7058
抄録: A Wiener-based inpainting quality prediction method is presented in this paper. The proposed method is the first method that can predict inpainting quality both before and after the intensities have become missing even if their inpainting methods are unknown. Thus, when the target image does not include any missing areas, the proposed method estimates the importance of intensities for all pixels, and then we can know which areas should not be removed. Interestingly, since this measure can be also derived in the same manner for its corrupted image already including missing areas, the expected difficulty in reconstruction of these missing pixels is predicted, i.e., we can know which missing areas can be successfully reconstructed. The proposed method focuses on expected errors derived from the Wiener filter, which enables least-squares reconstruction, to predict the inpainting quality. The greatest advantage of the proposed method is that the same inpainting quality prediction scheme can be used in the above two different situations, and their results have common trends. Experimental results show that the inpainting quality predicted by the proposed method can be successfully used as a universal quality measure.
Rights: Copyright ©2017 The Institute of Electronics, Information and Communication Engineers
Relation (URI): https://search.ieice.org/
資料タイプ: article
URI: http://hdl.handle.net/2115/67806
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 小川 貴弘

 

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