Optimal Training Sample Selection for Broadband Spectral Imaging System

The existing standard colorcharts or databases always have large sample size and suffer from color redundancy, which inevitability leads to a time-consuming process for practical spectral imaging. In order to resolve this problem, an optimal training sample selection method was proposed whose main idea was choosing the most effective samples from existing database based on error analysis of spectral reconstruction. A typical spectral imaging workflow was set up where the pseudoinverse (PSE) method was employed for spectral reconstruction and spectral root-mean-square error (RMS) was used as evaluation metric. Through minimizing the RMS error for each iteration, the method selected the optimal samples one by one from existing databases. The experimental results show that the proposed method has higher effectiveness both in spectral and colorimetric accuracy than the current existing methods when choosing the same number of training samples.