Reconstruction of spectral color information using weighted principal component analysis

Wu, Guangyuan; Liu, Zhen; Fang, Enyin; Yu, Hongliu

doi:10.1016/j.ijleo.2015.03.026

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…The 1269 Munsell Matt chips [ 27 ], 140 ColorChecker SG [ 35 ] and 354 Vrhel spectral datasets [ 36 ] are used in the simulation experiment. Firstly, The Munsell Matt chips are used as the training samples.…”

Section: Methodsmentioning

confidence: 99%

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Xiong

2023

Sensors

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The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples.

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Section: Methodsmentioning

confidence: 99%

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Xiong

2023

Sensors

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“…e Vrhel dataset contains 354 samples of reflectance spectra. e spectral reflectance function is proved to sample at 10 nm intervals without impacting greatly the mathematical precision [20]. So, the spectral reflectance of three different dataset samples range from 400 nm to 700 nm at 10 nm intervals.…”

Section: Experiments and Proceduresmentioning

confidence: 99%

Spectral Reflectance Recovery from Tristimulus Values under Multi-Illuminants

Qian

et al. 2019

Journal of Spectroscopy

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An efficient procedure for recovering spectral reflectance using an object’s tristimulus values under multi-illuminants is proposed by adapting with the characteristics of the testing sample to obtain the transformation matrix of pseudoinverse. Specifically, we propose the reference illuminants selection strategy and local sample weighted strategy to obtain the optimal transformation matrix under multi-illuminants condition. Selecting the reference illuminants are based on the result of the spectral angle mapper (SAM) statistics. The number of the selected local training samples and the weighted local samples can be determined by using the multicolor space Euclidean distance. To compare the experimental results, the proposed method significantly increases the spectral and colorimetric accuracy for the spectral reflectance recovery process.

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“…22 The number of eigenvectors can be reduced to three to compress the amount of data. [23][24][25][26][27] In order to improve the accuracy, Zhang et al further increased the principal components from three to nine. 4 As mentioned above, the spectral reconstruction problem involves two difficulties: input space sampling, inverse problem modeling and its solution.…”

Section: Introductionmentioning

confidence: 99%

“…The matrix R method is an important method for spectral information reconstruction, 18‐21 Cohen et al made great contributions to this method, and he applied principal component analysis (PCA) to reconstruct the spectral information 22 . The number of eigenvectors can be reduced to three to compress the amount of data 23‐27 . In order to improve the accuracy, Zhang et al further increased the principal components from three to nine 4 .…”

Section: Introductionmentioning

confidence: 99%