Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight

López-Álvarez, Miguel A.; Hernández‐Andrés, Javier; Valero, Eva M.; Romero, Javier

doi:10.1364/josaa.24.000942

Cited by 50 publications

(78 citation statements)

References 25 publications

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“…Suppose the response of the candidate sample is u, and then its corresponding reflectance r can be calculated according to the traditional Wiener estimation in Eq. (6). The training samples with reflectance r i for calculating K r can then be selected according to their spectral similarity to r .…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

et al. 2007

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Abstract:In multispectral imaging, Wiener estimation is widely adopted for the reconstruction of spectral reflectance. We propose an improved reflectance reconstruction method by adaptively selecting training samples for the autocorrelation matrix calculation in Wiener estimation, without a prior knowledge of the spectral information of the samples being imaged. The performance of the proposed adaptive Wiener estimation and the traditional method are compared in the cases of different channel numbers and noise levels. Experimental results show that the proposed method outperforms the traditional method in terms of both spectral and colorimetric prediction errors when the imaging channel number is 7 or less. When the imaging system consists of 11 or more channels, the color accuracy of the proposed method is slightly better than or becomes close to that of the traditional method.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…Recently, multispectral imaging has been widely studied in the reconstruction of the spectral information of the samples being imaged and the recovery of the spectrums of illuminants [1][2][3][4][5][6][7][8][9][10]. In a multispectral imaging system, usually more than three imaging channels are used.…”

Section: Introductionmentioning

confidence: 99%

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

et al. 2007

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“…In the figures, a square represents the points generated by one member of the reflectance pair, and a cross represents the points generated by the other member of the reflectance pair. measure of the similarity of two reflectances is the goodnessfitting coefficient (GFC) proposed by Romero et al [14], which is widely used to measure the similarity between two spectra [14][15][16][17]. GFC is defined as follows:…”

Section: Generation Of the Test Data Setmentioning

confidence: 99%

Extending “color constancy” outside the visible region

2011

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In this paper, the results of an investigation of the possibility of extending "color constancy" to obtain illuminantinvariant reflectance features from data in the near-ultraviolet (UV) and near-infrared (IR) wavelength regions are reported. These features are obtained by extending a blackbody-model-based color constancy algorithm proposed by Ratnasingam and Collins [J. Opt. Soc. Am. A 27, 286 (2010)] to these additional wavelengths. Ratnasingam and Collins applied the model-based algorithm in the visible region to extract two illuminant-invariant features related to the wavelength-dependent reflectance of a surface from the responses of four sensors. In this paper, this modelbased algorithm is extended to extract two illuminant-invariant reflectance features from the responses of sensors that cover the visible and either the near-UV or near-IR wavelength. In this investigation, test reflectance data sets are generated using the goodness-fitness coefficient (GFC). The appropriateness of the GFC for generating the test data sets is demonstrated by comparing the results obtained with these data with those obtained from data sets generated using the CIELab distance. Results based upon the GFC are then presented that suggest that the modelbased algorithm can extract useful features from data from the visible and near-IR wavelengths. Finally, results are presented that show that, although the spectrum of daylight in the near UV is very different from a blackbody spectrum, the algorithm can be modified to extract useful features from visible and near-UV wavelengths.

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“…6 There are many techniques to recover reflectance spectra from multispectral (typically color) information. 6,7 Some of the hyperspectral estimation techniques to recover reflectance spectra include Wiener estimation, multiple regression analysis, Maloney-Wandell method, Imai-Berns method, and Shi-Healey method. [6][7][8][9][10][11][12] Hyperspectral images have also been used to develop a hyperspectral estimation method from color and/or multispectral images for spectral estimation of paint.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging using RGB color for foodborne pathogen detection

Yoon

Shin

Lawrence

et al. 2015

J. Electron. Imaging

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Abstract. This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean R-squared value for hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight

Cited by 50 publications

References 25 publications

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

Reflectance reconstruction for multispectral imaging by adaptive Wiener estimation

Extending “color constancy” outside the visible region

Hyperspectral imaging using RGB color for foodborne pathogen detection

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