In the process of spectral reflectance reconstruction, sample selection plays an important role in the accuracy of the constructed model and in reconstruction effects. In this paper, a method for training sample selection based on camera response is proposed. It has been proved that the camera response value has a close correlation with the spectral reflectance. Consequently, in this paper we adopt the technique of drawing a sphere in camera response value space to select the training samples which have a higher correlation with the test samples. In addition, the Wiener estimation method is used to reconstruct the spectral reflectance. Finally, we find that the method of sample selection based on camera response value has the smallest color difference and root mean square error after reconstruction compared to the method using the full set of Munsell color charts, the Mohammadi training sample selection method, and the stratified sampling method. Moreover, the goodness of fit coefficient of this method is also the highest among the four sample selection methods. Taking all the factors mentioned above into consideration, the method of training sample selection based on camera response value enhances the reconstruction accuracy from both the colorimetric and spectral perspectives.
In order to improve the reconstruction accuracy and reduce the workload, the algorithm of compressive sensing based on the iterative threshold is combined with the method of adaptive selection of the training sample, and a new algorithm of adaptive compressive sensing is put forward. The three kinds of training sample are used to reconstruct the spectral reflectance of the testing sample based on the compressive sensing algorithm and adaptive compressive sensing algorithm, and the color difference and error are compared. The experiment results show that spectral reconstruction precision based on the adaptive compressive sensing algorithm is better than that based on the algorithm of compressive sensing.
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