Open AccessSequential adaptive estimation for spectral reflectance based on camera responses
Author(s) -
Lixia Wang,
Xiaoxia Wan,
Gensheng Xiao,
Jinxing Liang
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.389614
Subject(s) - computer science , reflectivity , digital camera , noise (video) , artificial intelligence , spectral density estimation , set (abstract data type) , optics , spectral imaging , computer vision , mathematics , image (mathematics) , physics , mathematical analysis , fourier transform , programming language
A sequential weighted nonlinear regression technique from digital camera responses is proposed for spectral reflectance estimation. The method consists of two stages taking colorimetric and spectral errors between training set and target set into accounts successively. Based on polynomial expansion model, local optimal training samples are adaptively employed to recover spectral reflectance as accurately as possible. The performance of the method is compared with several existing methods in the cases of simulated camera responses under three kinds of noise levels and practical camera responses under the self as well as cross test conditions. Results show that the proposed method is able to recover spectral reflectance with a higher accuracy than other methods considered.