Reconstruction Of Spectral Reflectance In Imaging Systems

With the rapid development of modern computer image processing technologies, color images and multispectral images have been widely used in the presentation, transfer and copying of color information. Different imaging systems have different characteristics, and all the color information they get and transfer are device-dependent, so it is necessary to find a way to get the device-independent color information. Spectral reflectance reconstruction, also referred as spectral characterization, aims to recover accurate spectral reflectance of object surface by employing standard color charts. This dissertation studies the methods for spectral reflectance reconstruction and the methods for selection of representative colors.When the imaging process is not a linear system or influenced by the measurement noise, the spectral reconstruction methods in hand cannot work very well. For instance, Wiener estimation only works well under linear systems. In order to cope with the nonlinearity and measurement noise, we studied the polynomial regression solved by ordinary least squares and regularized least squares. Experiment results show that, in terms of spectral and colorimetric error metrics, the regularized method performs better than Wiener estimation and ordinary polynomial regression.As there are always a large number of color samples on a color chart, spectral characterization becomes a time-consuming process for practical application. Some methods have been presented to selected representative color samples based on the redundancy of the colors on a chart. However, these methods only consider the distribution of spectral reflectance, and thus the selected colors may not be optimal for a specific imaging system. To deal with this problem, we proposed a sequential method for the selection of most representative colors, which consists of two steps. In the first step, a part of representative colors is selected according to the minimization of mean spectral root-mean-square error, by assuming a virtual imaging system. The spectral sensitivity of the real imaging system is then calculated based on these selected samples. In the second step, additional representative colors are selected based on the characteristics of the real imaging system. Experiment shows that the proposed method outperforms the previous methods in terms of both spectral and colorimetric accuracy.