Spectral reflectance recovery using convolutional neural network

Yifan Xiong; Guangyuan Wu; Xiaozhou Li; Shijun Niu; Xiaomeng Han

doi:10.1117/12.2628555

16 February 2022 Spectral reflectance recovery using convolutional neural network

Yifan Xiong, Guangyuan Wu, Xiaozhou Li, Shijun Niu, Xiaomeng Han

Proceedings Volume 12164, International Conference on Optoelectronic Materials and Devices (ICOMD 2021); 121640B (2022) https://doi.org/10.1117/12.2628555
Event: 2021 International Conference on Optoelectronic Materials and Devices, 2021, Guangzhou, China

Abstract

A procedure for spectral reflectance recovery from CIE tristimulus values is proposed using the convolutional neural network method. Unlike the common spectral recovery methods in a linear way, the nonlinear transformation from the CIE tristimulus values to spectral reflectance is to achieve in this paper. In consideration of the computation time and accuracy of spectral recovery, the internal parameters of convolutional neural network are adjusted by the number of neurons and the interval between neurons. The effectiveness of the proposed method and the previous methods are analyzed by calculating the spectral recovery accuracy under different spectral datasets and different error metrics. The results show that the proposed method is superior to traditional algorithms.

Citation Download Citation

Yifan Xiong, Guangyuan Wu, Xiaozhou Li, Shijun Niu, and Xiaomeng Han "Spectral reflectance recovery using convolutional neural network", Proc. SPIE 12164, International Conference on Optoelectronic Materials and Devices (ICOMD 2021), 121640B (16 February 2022); https://doi.org/10.1117/12.2628555

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KEYWORDS

Reflectivity

Neurons

Convolutional neural networks

Principal component analysis

Color difference

Seaborgium

Convolution

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