N
Nagaaki Ohyama
Researcher at Tokyo Institute of Technology
Publications - 258
Citations - 4721
Nagaaki Ohyama is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Color image & Image processing. The author has an hindex of 32, co-authored 258 publications receiving 4480 citations. Previous affiliations of Nagaaki Ohyama include National Institute of Information and Communications Technology.
Papers
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Journal ArticleDOI
High-quality recording of a full-parallax holographic stereogram with a digital diffuser
TL;DR: From experimental results, it is confirmed that the gray-level characteristic is greatly improved with the digital diffuser and high-resolution three-dimensional imagery is obtained with suppression of speckle noise.
Patent
Color image recording and reproducing system
TL;DR: In this paper, a spectral image photographing section photographs the image of an object to be photographed as spectrum information in units of pixels so as to mutually record and reproduce a faithful image between two points.
Journal ArticleDOI
Spectral reflectance estimation from multi-band image using color chart
TL;DR: This paper shows the requirement for the color chart based on the spectral characteristics of the set of target objects, and an investigation is carried out to roughly estimate the amount of the error when the colorchart does not satisfy the requirement completely.
Journal ArticleDOI
Color correction of pathological images based on dye amount quantification
TL;DR: A color correction method is proposed for hematoxylin and eosin stained pathological images in which the amounts of H&E dyes are estimated based on multispectral imaging technique and Beer Lambert law, and the color image is generated corresponding to the adjusted amount of dyes.
Journal ArticleDOI
DOI-PET image reconstruction with accurate system modeling that reduces redundancy of the imaging system
Taiga Yamaya,Naoki Hagiwara,Takashi Obi,Masahiro Yamaguchi,Kouichi Kita,Nagaaki Ohyama,Keishi Kitamura,Tomoyuki Hasegawa,Hideaki Haneishi,Hideo Murayama +9 more
TL;DR: A compressed imaging system model for DOI-PET image reconstruction, in order to reduce computational cost while keeping image quality, and results show that the proposed method followed by ML-EM reduces computational cost effectively while keeping the advantages of the accurate system modeling and DOI information.