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Single Image Haze Removal Using Iterative Ambient Light Estimation with Region Segmentation
http://hdl.handle.net/10131/00014010
http://hdl.handle.net/10131/00014010ce883d59-c850-4646-a76d-5c15f34e1ec6
名前 / ファイル | ライセンス | アクション |
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e104-a_2_550.pdf (31.2 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-07-13 | |||||
タイトル | ||||||
タイトル | Single Image Haze Removal Using Iterative Ambient Light Estimation with Region Segmentation | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | haze removal, dark channel, HSV color space, region segmentation | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Araki, Yuji
× Araki, Yuji× Mita, Kentaro× Ichige, Koichi |
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著者所属 | ||||||
Department of Electrical and Computer Engineering, Yokohama National University | ||||||
著者所属 | ||||||
Department of Electrical and Computer Engineering, Yokohama National University | ||||||
著者所属 | ||||||
Department of Electrical and Computer Engineering, Yokohama National University | ||||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Copyright(C)2021 IEICE | |||||
書誌情報 |
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 巻 E104.A, 号 2, p. 550-562, 発行日 2021-02-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1745-1337 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1587/transfun.2020EAP1038 | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
出版者 | ||||||
出版者 | IEICE | |||||
関係URI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1587/transfun.2020EAP1038 | |||||
関連名称 | https://doi.org/10.1587/transfun.2020EAP1038 |