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非可控条件下基于深度神经网络的彩色图像光谱估计

赵微 刘洋 陈建宇 莫庆伟 丁银萍 徐鹏

影像科学与光化学2024,Vol.42Issue(2):83-88,6.
影像科学与光化学2024,Vol.42Issue(2):83-88,6.DOI:10.7517/issn.1674-0475.231011

非可控条件下基于深度神经网络的彩色图像光谱估计

Spectral Estimation from Color Images Based on Deep Neural Network under Uncontrolled Conditions

赵微 1刘洋 1陈建宇 1莫庆伟 2丁银萍 1徐鹏1

作者信息

  • 1. 浙江农林大学暨阳学院,浙江诸暨 311800
  • 2. 浙江老鹰半导体技术有限公司,浙江诸暨 311800
  • 折叠

摘要

Abstract

Spectral reflectance can be estimated from color images,and currently,spectral estimation based on color images is generally carried out under controlled conditions,that is,the light source and the camera settings of color cameras are strictly fixed,so as to ensure that the main factor causing the change of camera response values is only the spectral reflectance,so that the mapping relationship between camera response values and the spectral reflectance can be established accurately.However,there are many factors causing the variation of camera response values under uncontrolled conditions,which increases the difficulty of spectral estimation.In this paper,a deep neural network framework is designed based on the residual neural network with the help of its powerful nonlinear fitting ability to estimate the spectral reflectance from the RGB values of color images.A large number of color images were collected by smart phones under uncontrolled conditions,and the proposed network was verified by five-fold cross validation.The results show that the proposed deep neural network framework can achieve higher accuracy than two existing spectral estimation algorithms,which enables more accurate spectral estimation based on color images under uncontrolled condi-tions.

关键词

光谱估计/彩色图像/深度神经网络/非可控条件

Key words

spectral estimation/color images/deep neural network/uncontrolled conditions

引用本文复制引用

赵微,刘洋,陈建宇,莫庆伟,丁银萍,徐鹏..非可控条件下基于深度神经网络的彩色图像光谱估计[J].影像科学与光化学,2024,42(2):83-88,6.

基金项目

国家自然科学基金(62005246) (62005246)

浙江农林大学暨阳学院科研发展基金(JY2018RC02,RC2021A09) (JY2018RC02,RC2021A09)

浙江农林大学暨阳学院大学生创新训练计划项目(202303) (202303)

国家级大学生创业训练计划项目(202313283003) (202313283003)

影像科学与光化学

OACSTPCD

1674-0475

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