首页    期刊浏览 2024年10月06日 星期日
登录注册

文章基本信息

  • 标题:Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
  • 本地全文:下载
  • 作者:Jason Deglint ; Farnoud Kazemzadeh ; Daniel Cho
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2016
  • 卷号:6
  • 期号:1
  • DOI:10.1038/srep28665
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:The simultaneous capture of imaging data at multiple wavelengths across the electromagnetic spectrum is highly challenging, requiring complex and costly multispectral image devices. In this study, we investigate the feasibility of simultaneous multispectral imaging using conventional image sensors with color filter arrays via a novel comprehensive framework for numerical demultiplexing of the color image sensor measurements. A numerical forward model characterizing the formation of sensor measurements from light spectra hitting the sensor is constructed based on a comprehensive spectral characterization of the sensor. A numerical demultiplexer is then learned via non-linear random forest modeling based on the forward model. Given the learned numerical demultiplexer, one can then demultiplex simultaneously-acquired measurements made by the color image sensor into reflectance intensities at discrete selectable wavelengths, resulting in a higher resolution reflectance spectrum. Experimental results demonstrate the feasibility of such a method for the purpose of simultaneous multispectral imaging.
国家哲学社会科学文献中心版权所有