An improved spectral estimation method based on color perception features of mobile phone camera

• Published in: Frontiers in neuroscience Vol. 16; p. 1031505
• Main Authors: Liu, Duan, Wu, Xinwei, Liang, Jinxing, Wang, Tengfeng, Wan, Xiaoxia
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 19.10.2022
• Subjects: CIELAB uniform color space; color perception feature; mobile phone camera; Neuroscience; spectral estimation; spectral imaging; spectral reflectance
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.1031505

We use the mobile phone camera as a new spectral imaging device to obtain raw responses of samples for spectral estimation and propose an improved sequential adaptive weighted spectral estimation method. First, we verify the linearity of the raw response of the cell phone camera and investigate its feasibility for spectral estimation experiments. Then, we propose a sequential adaptive spectral estimation method based on the CIE1976 L*a*b* (CIELAB) uniform color space color perception feature. The first stage of the method is to weight the training samples and perform the first spectral reflectance estimation by considering the Lab color space color perception features differences between samples, and the second stage is to adaptively select the locally optimal training samples and weight them by the first estimated root mean square error (RMSE), and perform the second spectral reconstruction. The novelty of the method is to weight the samples by using the sample in CIELAB uniform color space perception features to more accurately characterize the color difference. By comparing with several existing methods, the results show that the method has the best performance in both spectral error and chromaticity error. Finally, we apply this weighting strategy based on the CIELAB color space color perception feature to the existing method, and the spectral estimation performance is greatly improved compared with that before the application, which proves the effectiveness of this weighting method.