Abstract
The influence of light sources on digital camera-based spectral estimation is explored. The CIE daylight and non-CIE daylight illuminants with different Correlated Color Temperature (CCT) are first tested comparatively, results indicate that CCT can be used to describe the performance of the CIE daylight illuminants for spectral estimation but not applicable to all types of light sources. To further investigate the mechanism of light effects on spectral estimation, several handmade special shape of Spectral Power Distribution (SPD) are tested, results show that the red component in visible spectrum is crucial for a higher spectral estimation accuracy. Finally, several feature metrics of SPD are proposed to evaluate the performance of the light sources in spectral estimation, results illustrate that the better the feature metrics the better the spectral estimation accuracy.
Highlights
The surface spectral reflectance is the unique identity of an object, and at the same time, it is regarded as the “fingerprint” of the object color information
The current optimization researches especially for the optimization of light sources are performed without prior knowledge, which means that how the light sources will influence the spectral estimation is not known initially and the optimal light source was often selected from countless possible Spectral Power Distribution (SPD) based on exhausted searches or using some searching algorithms
The investigation between Correlated Color Temperature (CCT) and spectral estimation errors indicated that the CCT can be used to describe the performance of the CIE daylight illuminants in spectral estimation, but can not be used to describe the other types of light sources
Summary
The surface spectral reflectance is the unique identity of an object, and at the same time, it is regarded as the “fingerprint” of the object color information. Digital camera-based spectral estimation has become a valuable solution for fast and high spatial resolution spectral imaging with low-cost its accuracy is affected by some factors [6,7,8]. The workflow of digital camera-based spectral estimation for spectral imaging is plotted as Fig. 1. For digital camera-based spectral estimation, the training samples with known spectral reflectance are first captured under the illumination of the light source, and the spectral estimation matrix is calculated based on the training sample data and spectral estimation algorithm (SRA) [9]. During the workflow of digital camera-based spectral estimation, the light source is an important component that influence the spectral estimation accuracy. Different light sources will lead to different camera responses, and lead to different spectral estimation matrix, which in turn lead to the different spectral estimation accuracy
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