Abstract
An improved spectral reflectance estimation method is developed to transform raw camera RGB responses to spectral reflectance. The novelty of our method is to apply a local weighted linear regression model for spectral reflectance estimation and construct the weighting matrix using a Gaussian function in CIELAB uniform color space. The proposed method was tested using both a standard color chart and a set of textile samples, with a digital RGB camera and by ten times ten-fold cross-validation. The results demonstrate that our method gives the best accuracy in estimating both the spectral reflectance and the colorimetric values in comparison with existing methods.
Highlights
Digital cameras can be used to provide spectral data for many applications and the development of algorithms to calculate these spectral data from image RGB data is of prime importance
Different digital camera-based spectral imaging systems have been developed for practical applications, such as a camera with bandpass filters [1,2,3], a camera utilizing multiple illuminants [1,4,5], and a camera with three-channel responses under a specific illuminant [6,7,8,9,10,11,12,13]
Accurate spectral and colorimetric estimation are both critical for practical applications and, since the digital RGB values are readily available from an image, an algorithm that accurately estimates the spectral reflectance from these RGB camera responses can be very useful
Summary
Digital cameras can be used to provide spectral data for many applications and the development of algorithms to calculate these spectral data from image RGB data is of prime importance. Different digital camera-based spectral imaging systems have been developed for practical applications, such as a camera with bandpass filters [1,2,3], a camera utilizing multiple illuminants [1,4,5], and a camera with three-channel responses (but a single RGB image) under a specific illuminant [6,7,8,9,10,11,12,13]. Accurate spectral and colorimetric estimation are both critical for practical applications and, since the digital RGB values are readily available from an image, an algorithm that accurately estimates the spectral reflectance from these RGB camera responses can be very useful
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