The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini mencadangkan teknik Depth From Defocus (DFD) novel berdasarkan sifat bahawa dua imej yang mempunyai tetapan fokus berbeza bertepatan jika ia diulang dengan tetapan fokus bertentangan, yang dirujuk sebagai sifat "ulang semula silang" dalam kertas kerja ini. Berdasarkan sifat, teknik yang dicadangkan menganggarkan profil kedalaman dari segi blok untuk objek sasaran dengan meminimumkan ralat kuasa dua min antara imej bersilang kabur. Tidak seperti teknik DFD sedia ada, teknik yang dicadangkan adalah bebas daripada parameter kanta dan bebas daripada model fungsi penyebaran titik. Teknik pampasan untuk kemungkinan penyahjajaran piksel antara imej juga dicadangkan untuk meningkatkan ketepatan anggaran kedalaman. Keputusan percubaan dan perbandingan dengan teknik DFD yang lain menunjukkan kelebihan teknik kami.
Kazumi TAKEMURA
University of Fukui
Toshiyuki YOSHIDA
University of Fukui
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Salinan
Kazumi TAKEMURA, Toshiyuki YOSHIDA, "Depth from Defocus Technique Based on Cross Reblurring" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2083-2092, November 2019, doi: 10.1587/transinf.2019PCP0004.
Abstract: This paper proposes a novel Depth From Defocus (DFD) technique based on the property that two images having different focus settings coincide if they are reblurred with the opposite focus setting, which is referred to as the “cross reblurring” property in this paper. Based on the property, the proposed technique estimates the block-wise depth profile for a target object by minimizing the mean squared error between the cross-reblurred images. Unlike existing DFD techniques, the proposed technique is free of lens parameters and independent of point spread function models. A compensation technique for a possible pixel disalignment between images is also proposed to improve the depth estimation accuracy. The experimental results and comparisons with the other DFD techniques show the advantages of our technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019PCP0004/_p
Salinan
@ARTICLE{e102-d_11_2083,
author={Kazumi TAKEMURA, Toshiyuki YOSHIDA, },
journal={IEICE TRANSACTIONS on Information},
title={Depth from Defocus Technique Based on Cross Reblurring},
year={2019},
volume={E102-D},
number={11},
pages={2083-2092},
abstract={This paper proposes a novel Depth From Defocus (DFD) technique based on the property that two images having different focus settings coincide if they are reblurred with the opposite focus setting, which is referred to as the “cross reblurring” property in this paper. Based on the property, the proposed technique estimates the block-wise depth profile for a target object by minimizing the mean squared error between the cross-reblurred images. Unlike existing DFD techniques, the proposed technique is free of lens parameters and independent of point spread function models. A compensation technique for a possible pixel disalignment between images is also proposed to improve the depth estimation accuracy. The experimental results and comparisons with the other DFD techniques show the advantages of our technique.},
keywords={},
doi={10.1587/transinf.2019PCP0004},
ISSN={1745-1361},
month={November},}
Salinan
TY - JOUR
TI - Depth from Defocus Technique Based on Cross Reblurring
T2 - IEICE TRANSACTIONS on Information
SP - 2083
EP - 2092
AU - Kazumi TAKEMURA
AU - Toshiyuki YOSHIDA
PY - 2019
DO - 10.1587/transinf.2019PCP0004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - This paper proposes a novel Depth From Defocus (DFD) technique based on the property that two images having different focus settings coincide if they are reblurred with the opposite focus setting, which is referred to as the “cross reblurring” property in this paper. Based on the property, the proposed technique estimates the block-wise depth profile for a target object by minimizing the mean squared error between the cross-reblurred images. Unlike existing DFD techniques, the proposed technique is free of lens parameters and independent of point spread function models. A compensation technique for a possible pixel disalignment between images is also proposed to improve the depth estimation accuracy. The experimental results and comparisons with the other DFD techniques show the advantages of our technique.
ER -