N. Shimano

dblp:24/525 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 77% Image and video processing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › reflectance acquisition
spectral reflectance estimation
0.112006
Recovery of Spectral Reflectances of Objects Being Imaged Without Prior Knowledge · IEEE Trans. Image Process. 2006
Image and video processing › image restoration › image denoising
noise estimation
0.012006
Recovery of Spectral Reflectances of Objects Being Imaged Without Prior Knowledge · IEEE Trans. Image Process. 2006

Methods — techniques the papers use, named apart from their topics

wiener filter · 0.1noise variance modeling · 0.1
YearPublicationVenuePosition
2006 Recovery of Spectral Reflectances of Objects Being Imaged Without Prior Knowledge
abstract
Prior knowledge of the noise present in a color image acquisition device is very important in estimating colorimetric values or in recovering the spectral reflectances of pixels of objects being imaged, since these values are greatly influenced by the noise. In this paper, a new model is proposed for the determination of the noise variance of a multispectral color image acquisition system and experimental results to demonstrate its accuracy are presented. It is demonstrated that the noise variance of an actual multispectral color image acquisition system computed by the proposal agrees fairly well with the variance which minimizes the mean-square error of the recovered reflectances by the Wiener filter. As an application of the proposal, it is shown that spectral reflectances of an art painting are recovered accurately by the use of sensor responses without prior knowledge of objects being imaged and noise present in an image acquisition system.
N. Shimano
IEEE Trans. Image Process.1