This paper proposes a compact and reliable method to estimate the bispectral Donaldson matrices of fluorescent objects by using multispectral imaging data. We suppose that an image acquisition system allows multiple illuminant projections to the object surface and multiple response channels in the visible range. The Donaldson matrix is modeled as a twodimensional array with the excitation range (350, 700 nm) and the reflection and emission ranges (400, 700 nm). The observation model is described using the spectral sensitivities of a camera and the spectral functions of reflectance, emission, and excitation. The problem of estimating the spectral functions is formulated as a least squares problem to minimize the residual error of the observations and the roughness of the spectral functions. An iterative algorithm is developed to obtain the optimal estimates of the whole spectral functions. The performance of the proposed method is examined in simulation experiments using multispectral imaging data in detail.
Digital camera-based spectral estimation in open environment is a challenge in current stage. Although some methods have been proposed in recent years, the methods do not consider the exposure inconsistency between camera spectral characterization and spectral estimation applications, that makes the proposed method cannot for practical applications. We proposed here a spectral estimation method based on imaging condition correction of which can deal with the problem exist in current methods. Using the whiteboard and raw camera response, the imaging conditions of open environment is recorded and corrected to the reference imaging conditions, and the surface spectral of object is estimated using the established spectral estimation matrix in the reference imaging conditions. The proposed method in three application models are tested and compared. The result shows that the adaptive model for imaging condition correction gives the best spectral estimation accuracy.