Literature DB >> 16830907

Recovery of spectral reflectances of objects being imaged without prior knowledge.

Noriyuki Shimano1.   

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.

Mesh:

Year:  2006        PMID: 16830907     DOI: 10.1109/tip.2006.877069

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  3 in total

1.  Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes.

Authors:  Yuqi Li; Aditi Majumder; Hao Zhang; M Gopi
Journal:  Sensors (Basel)       Date:  2018-04-12       Impact factor: 3.576

2.  Differentiating Biological Colours with Few and Many Sensors: Spectral Reconstruction with RGB and Hyperspectral Cameras.

Authors:  Jair E Garcia; Madeline B Girard; Michael Kasumovic; Phred Petersen; Philip A Wilksch; Adrian G Dyer
Journal:  PLoS One       Date:  2015-05-12       Impact factor: 3.240

3.  Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling.

Authors:  Jason Deglint; Farnoud Kazemzadeh; Daniel Cho; David A Clausi; Alexander Wong
Journal:  Sci Rep       Date:  2016-06-27       Impact factor: 4.379

  3 in total

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