Spectral Estimation of Printed Colors Using a Scanner, Conventional Color Filters and Applying Backpropagation Neural Network

econstructing the spectral data of color samples using conventional color devices such as a digital camera or scanner is always of interest. Nowadays, multispectral imaging has introduced a feasible method to estimate the spectral reflectance of the images utilizing more than three-channel imaging. The goal of this study is to spectrally characterize a color scanner using a set of conventional color filters. To this end, a 1355 chart was generated and printed; the images of the printed charts were scanned putting a translucent color filter in front of each page during scanning process. Each page was scanned with 4 color filters, including gray, blue, green and yellow ones. A feed-forward Back-Propagation neural network with 12 input neurons of camera responses, one hidden layer containing 20 neurons, and an output layer of 31 neurons of spectral reflectance values was applied. It was shown that it is accurately possible to estimate the spectral data of printed samples from the scanner responses using conventional color filters and the proposed NN with an average GFC value of 0.999. The mean of color difference error was about 0.612 CIEDE2000 (1:1:1) unit or 0.987 CIELAB unit. Prog.